{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 任务描述\n",
    "数据说明：\n",
    "Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集） 根据现有的医疗信息预测5年内皮马印第安人糖尿病发作的概率。   \n",
    "\n",
    "数据集共9个字段: \n",
    "0列为怀孕次数；\n",
    "1列为口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度；\n",
    "2列为舒张压（单位:mm Hg）\n",
    "3列为三头肌皮褶厚度（单位：mm）\n",
    "4列为餐后血清胰岛素（单位:mm）\n",
    "5列为体重指数（体重（公斤）/ 身高（米）^2）\n",
    "6列为糖尿病家系作用\n",
    "7列为年龄\n",
    "8列为分类变量（0或1）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据与数据概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes = pd.read_csv(\"diabetes.csv\")\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8个特征：怀孕次数，血糖，血压，皮脂厚度，胰岛素，BMI身体质量指数，糖尿病遗传函数，年龄。\n",
    "“结果”是我们将要预测的特征，0意味着未患糖尿病，1意味着患有糖尿病。在768个数据点中，500个被标记为0,268个标记为1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "768个样本，除了结果，均为数值型数据。表面上看没有空值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "diabetes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0。而在一些特定列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。\n",
    "具体来说，下列变量的最小值为0时数据无意义： 1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose          0\n",
      "BloodPressure    0\n",
      "SkinThickness    0\n",
      "Insulin          0\n",
      "BMI              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((diabetes[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示1、血浆葡萄糖浓度 2、舒张压 3、肱三头肌皮褶厚度 4、餐后血清胰岛素 5、体重指数，这五个特征中分别为零的个数。数据处理时需要将对这些值进行缺失值填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimensions of diabetes data:(768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"dimensions of diabetes data:{}\".format(diabetes.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索与特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理\n",
    "从数据预览的结果中我们可以看到很多列的最小值为0。而在一些特定列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。\n",
    "\n",
    "具体来说，下列变量的最小值为0时数据无意义：\n",
    "1、血浆葡萄糖浓度\n",
    "2、舒张压\n",
    "3、肱三头肌皮褶厚度\n",
    "4、餐后血清胰岛素\n",
    "5、体重指数\n",
    "\n",
    "在Pandas的DataFrame中，通过replace()函数可以很方便的将我们感兴趣的数据子集的值标记为NaN。\n",
    "\n",
    "标记完缺失值之后，可以利用isnull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "diabetes[NaN_col_names] = diabetes[NaN_col_names].replace(0, np.NaN)\n",
    "print(diabetes.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用中位数进行填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = diabetes.median() \n",
    "diabetes = diabetes.fillna(medians)\n",
    "\n",
    "print(diabetes.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看结果数据分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outcome\n",
      "0    500\n",
      "1    268\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(diabetes.groupby('Outcome').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203eafd29b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eafa5518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(diabetes['Outcome'],label = \"Count\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 怀孕次数数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb172908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(diabetes.Pregnancies)\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "从常识上看，怀孕次数太大的数据可能是噪声点；\n",
    "删除怀孕次数大于10的样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(710, 9)\n"
     ]
    }
   ],
   "source": [
    "ulimit = 10\n",
    "diabetes = diabetes[diabetes['Pregnancies'] < ulimit]\n",
    "print (diabetes.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 葡萄糖浓度数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb216ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(diabetes.Glucose, kde = False)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "葡萄糖浓度和是否得病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eacd8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Glucose', data=diabetes, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度越高，得糖料病的概率越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 葡萄糖浓度数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb505320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(diabetes.BloodPressure, kde = False)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "探索血压高低和是否得病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb41f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BloodPressure', data=diabetes, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中看，舒张压和糖料病的关系不明显"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三头肌皮褶厚度数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of SkinThickness')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb6764e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(diabetes.shape[0]),diabetes[\"SkinThickness\"].values,color='purple')\n",
    "plt.title(\"Distribution of SkinThickness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图中的异常值点在医学领域可能代表得病，不能删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r08l8lYp25eXltT5u7gIJCzuXrl3hxDtnlAqCkpKSSJeglPLj3Llz5x/H0ge8QMLiC2DmRdv+N9rYHTTFxcXnH8fSvVClmpKav6c1Hzd3gcwN9SPgIxG5G9gPdAfaADeGorBYdObMmQseZ2bGxJRYSjUpp0+fPv+4sLAwgpWEVyCD8raLSE9gEtAReAdYZIw5G6riYk3NH8LTp09rWCgVhY4dOwZAK7s5/zgWBHJlgS8Y3ghRLTGv5qeUmsGhlIoeeXl52C3QK8XJ4cOHIl1O2Pgbwb3MGDPe9/hTLh3NDYAx5roQ1BZzThUW4k5IxVJ+OqYub5VqSvbu2UOnJBedk1ysP3CS0tJSkpOTI11WyPm7sphb4/Ff6txLBcWpU4V4EtM1LJSKUk6nk917djMqo4quLb0zLuzYsYPBgwdHuLLQ8zeC+581Hs8JfTmxy+12U1pSjKdtZ8TmoKioKNIlKaUusn37diornfRuXUWPVi5scbBx40YNi4uJyDhgAJBUc7sx5ufBLCoWFRcXY4zB2BIwtgRts1AqCq1duxZLHPRu7fK1W1Sx5rPVzJw5s9mvmhfIdB9/BP4BXIO3N1TNL9VI1bedjC0Rl8Wht6GUijLGGFatXMGVrZ0kWr3NtzkZlRw5eoz9+/dHuLrQC+TKYgowwBiTF6piYln1lYTHloCxJXKy4FSEK1JK1bRt2zZOnCzgtt7fzAeVk+Hk9T2wfPlyunfvHsHqQi+QEdyFwBm/e6kGOXnSu3KssbfAY0+k6HRhTK3CpVS0W7ZsGfFWISfjm9kVWtoN/dOcfPjBsma/xMBlw0JEulZ/Ab/Du6Ld0Jrbfa+pRjpx4gSIYOyJmPgk3G633opSKkqUlZWR+/FyBmWU47jofszIdhWcLjrDunXrIlNcmDRk8aNJFz03gCU45cSu/Px8cCSDxOGJb3l+W0ZGRoQrU0rl5uZSXlHJ6PaXTkneP62KlHhYtHAhw4cPj0B14XHZKwtjTFw9vjQoguDAwYO47K0A8Di8/x46FDujQ5WKZosWvk+HJA/dW156q8kaByPblrFu3ToKCgoiUF14+G2zEJGt4SgkllVWVpKfl4c7MRXwtluIzcHevXsjXJlS6sCBA+zavYfR7cqpq3fsde0q8RjD0qVLw1tcGNWngbtLqIuIdbt378bj8eBu4bvlJEJVQhrbtm+PbGFKKZYuXYolDoa1qXtVvDaJHnq3drFs6RKMqXVWpCavPmHRPL/zKLJ582YA3MnfzDLrTm7Doa+/vmDacqVUeLlcLj76YBkD0ypJtl/+T+HItuUcPXacrVub582Y+oRFooh8crmvkFfZzK1btx7TIh2s3yw66GrZAfBOJaCUioyNGzdypqSU4W0vvKr4x55E/rEn8YJt12Y6ibcKH330UThLDJv6DMqrAv4a6kJiVWFhITt2bMfZbsAF2z0t0sGeyCeffMINN9wQoeqUim0ffvghSXbol3bhitKHz176pzPeAtekVbAi92Mefvhh7HZ7uMoMi3qFhU4iGDq5ubkYY3ClZhN/+HMAKjsNARGcKV1Ys2ZtzEyBrFQ0KSsr47PPVjMsvQJrPYcvD21byZqvytiwYUOz60Zbn/8EzXt2rAgyxvD++wvxJGXiSUghruw0cWXfTCBYld4dl6uq2V7WKhXN1qxZQ2Wlk6GXadi+WN/WVSTbvdN/NDf1CYvnQ15FjNq0aRN5eYepzOhV6+ueFul4kjJ4e8ECnfpDqTD7ePlyUh3QM6X+03hY4+DajArWfLaasrKyEFYXfn7Dwhjz/6ofi8g4EXlSRP5Pza/Qlth8/etf/0LsibhSs+vcpzKzD0ePHOHzzz8PY2VKxbaSkhI2bNjA4Ixy4gK8tzK0TSWVzirWrFkTmuIiJBhTlGeFprTm7cCBA6xfv56KjCsgru6mI1frbIhPYv4buvS5UuHyySef4HK7GRLALahqPVq5SHV4r0yak6iYolxELMBG4IgxZpKIZANvAKnAJuBuY4zzcsdoat566y3EYsOZecXld4yLoyKzD1u3rGfXrl1ccYWf/ZVSjfbxx8tp28LQJdkd8HvjBAZnlvPhhvWUlJTQsmXLEFQYftEyRfmjwM4az38NvGiM6QEUAfeH6LwRUVpayvLly6lM7XrB2Iq6VKX3RCw23n333TBUp1RsO336NF9t/opBGXVP7+HP4MxK3G4Pq1evDm5xERRIWIRkinIRyQJuBv7iey7AGOBt3y5zgG815hzRZuXKlVRVVVFVR8P2Jax2Klt3YcXKlVRUVIS2OKVi3KefforHGAZnNvxmRnaym4xEw8qVK4JYWWQFEhav4J2e/DO8U5dXfzV2trv/Ap4Eqrv7pAFnjDHVXRDygQ6NPEdUWf3ZZ+BoiScxrd7vcaV2pbKigi+//DKElSmlPlm1irYtDFktAr8FVU0EctIr2PTFJkpLS4NYXeTUOyxCMUW5iEwCThpjvqi5ubbT1/H+GSKyUUQ2NpWpgY0xbNmyBWdyOwK5xnUntwGJY9u2bSGsTqnYVlJSwuavNnNNWkWDb0FVuzbDicvtZu3atcEpLsICubIIheHArSLyNd4G7TF4rzRSRKS68T0LOFrbm40xrxpjcowxOU1lkaCCggLKy8oCuqoAIM6KSUjhwIEDoSlMKcXatWtxuz3kNOIWVLWuLV2kOGg27RaX7Q0lIsuMMeN9jz+ljk/4xpjrGnJyY8xPgJ/4jj8aeMIYM1VE3gLuwBsg9wDvNeT40aj6CshjbxHwe922Fhw/cSLYJSmlfFauWEGqA7KTG7+edpx454pa/flaysrKSExM9P+mKOav6+zcGo//EspCLvJj4A0ReRb4kmY0kWH1/UtjjQ/4vcYaT2lJcbBLUkrxzUC8GzsEPhCvLkPaVPLxEQdr1qxp8hOCXjYsjDH/rPH0HWPMJS01ItIpGIUYY1YCK32PDwCDgnHcaHPu3DnvA0vgM1Iai51zZ88FuSKlFMCSJUtwud2XXeQoUD1auchIMLz/3rtNPiwCabNYLCIXfBz2dZtdFdySmreioiIATD3GV1zMWOMpLyvD6WxW4xOVijin08mCt9+id2sXnRswEK8ucQI3dihjy9Zt7NixI2jHjYRAwmId8G51w7OI9MR7JfBcCOpqtg4dOoRY7Q26DeVxeEeCHj58ONhlKRXTXn/9dQpOFXJLZ/+T//1jTyKHSi0cKrXw/KaWlyyCdLFR7StoGQ8v/v73uFyNbwuJlEC6zv4H3jEP80XkSiAX+KkxJpxtGU2ay+VizZq1VLVoE1C32WrupDYAzW6CMqUiadu2bcybN4/hbSu4MrXK7/6Hz1opd8dR7o5j1xlbrQsh1ZRghXt6lLJ33z5mz54dpKrDL9CuszMAN7AeeNwYM9fP/qqG999/n8LCUzjrO3L7IiY+CVerDrz11tsUFhYGuTqlYs/27dt58j+eIN3hZmqP0E0pfm2mk5HtKvjHP/7BvHnzQnaeULpsWIjIpxettb0K6AKUAQ/pGtz1t2HDBl5++RVcrbJwp3Rs8HEqOw7ibFkZP/v5z5vNyFClImHVqlU88fhjJEs5Tw84Q5Kt1pEBQXNfr3MMa1PJa6+9xssvv0xlZfAa0sPBX9dZvcXUSG63m3feeYdXXnkFd0JrKrKva9AtqGqehNaUdRnJjp2rmPnQQ/z8Zz+jR48eQaxYqeatuLiYl176L3JzV5Dd0s2jV5WQGh/6xcUscTCjz1kSrIY333yTdZ+v5elnfkqvXg270xBu/rrOzhGRa4BKY8w2ABHJxDvKui/wOfB4yKtsorZs2cJ///d/s3fvXtwpHSnrOqpBXWYv5krNpszqIP/gSmbMmMHtt9/OXXfdRUpKShCqVqp5cjqdLF68mLlzZlNSXMzt2WXc3Lm83utrB0OcwD29zjEg3cnfdufx4IMPcuutt3LXXXeRnp4evkIaQIy5/KWXb+T2L40xy33P38U7sd9svGtcbDHGzAxxnX7l5OSYjRs3RroM3G4369atY968f7J9+zawJ1KeNci7Gp6fK4qEXUsAKL9iYv1O5qokPn8j9oLd2Ox2bpk0icmTJ9OuXbvGfhtKNRtVVVUsXbqU1+fOoeBUIb1SXNzV42yDu8g+v6klu87Yzj+/IqWKpweWBHycc1XCm/sT+eSYgzirldtu+xZTpkwhLS3AqYAaSUS+MMbk+N2vHmFxCuhgjKkUkRSgAOhrjNkjIh2BNcaYht+ED5JIh8XRo0dZtmwZi5cspfBUAcQnUdHmSqrSe4KlfmtMBRwWPnHlZ7Af34qtcD9gGHj1QCZOnMDIkSOJjw+8i65SzUFpaSmLFy/mnbff4uSpQrq3cnN79ln6tHY1apLAYIVFtYLyON77OoHVxx3YbDbG3TSeO+64g86dOze8yADUNyzq81fMClSPAhsCHDPG7AEwxuT5AiQmFRQUsGrVKnJzV7Bjx3YAXK2yqOo2GldKF4gLz/WtJyGFiuyRVHYYiO3UXjbt2MOmTV+QkJDIiBHDGTNmDDk5OdhsNv8HU6qJy8vLY8GCBSxbupSKykp6t3YxrX8ZV6VWNXom2VDISPDwQO9z3NK5nEWHEli2eCELFy5k0KBrmTz5u+Tk5CBRUHh9riw+A14yxrwpIrMBjzHmPt9rHYB1xpiIr8MdriuLkydP8umnn7JixYrz04WbFmk4U7pQldYNE5/UoOPGH/4c2ynv0iDuxDQ8ialUdhrSsCKNwVJ6DGvhfuLPHMa4Kkls0YKRI0YwatQocnJysNsb33aiVDTZtm0bb8yfz2drPsMiMDSzgnEdK4I6IhuCf2VxsRKnkHvEwcdHEymuhOwunblzyvcZM2ZMSD7wBfM21AhgId4ZZ93ACGPMbt9rjwGDjTHfa3zJjRPKsCgsLCQ3N5fcFSvY6RuybxJTcbbuQlXrbExCq0afI2HXEqylx88/dyW3Dfh2VK08biwlR7GdPoi9OA/jqiQhIZHhw4cxZswYBg0ahNUayFLsSkUPj8fD2rVrmf/Pf7Jt+3Za2GBshzJu6FBBSnxousKGOiyqVXng8xPxLMlL5MjZODLS05j83e8xadKkoM5gG7Sw8B0sGegJ7Kk5maCI9AJKjTG1rjcRTsEOi8rKSlasWMEHH37I5i+/xBjzzRVE6y5BCYiaQhYWNfmCw1p0iPjiw5iqCpKSkxk7ZgwTJkzgiiuuCO75lAqh/Px8fvub3/DVli2kJxjGZ5VxXbsKHCH+7BOusKhmDGwptLH4cCK7zlhJa53C4//xJMOGDQvK8YPZZoEvIL6oZfvuBtQW1UpLS3n33Xd56+0FlBSfAUdLKtv1pyq1W9ADIuziLLhTOuJO6Uilx42l5AhVhft5f9Fi3nvvPfr168/3vz+FwYMHR8U9UqVq4/F4WLBgAX957VUspop7e53lunaVWCK9lFuIiED/9Cr6pxezt9jK7N0enn76aW644QYeeeQRWrZsGZY69P5DDVu3buWZn/6MkuIzuFpl4ew1FHdy20YNootacRbcKZ1wp3Siwu3EVrCHLbt3sOWppxgxYgRPP/10k1+sRTVPH3zwAX/605/on+bk3ivOhWVAXbTo0crFL3OKeP/rBBZ+vJzy8jKee+75sJy7mWZx4Pbt28cPf/hDSpwezvW5lfKe43C3DGyd7EZxO3E4HNxxxx04HA5wh3EacoudqrZXUnrlHVRkXcvqzz7jqad+Er7zKxWAhQvfp30Lw2P9SiMSFOUuueB3tdwV3g+T1jj4TtdyJnQqZ+3az8M2T5yGhc/OnTtxu92c6zYWT4vwj6QUl5NJkyYxa9Ysbr75ZsQVgTUr4uKoancVzoxebNu+jfq0ZykVTlVVVezZvYcEixtXhH48y1xywe9qWZjDopo9zuDxeNi5c2dYzqe3oXy6du1KXJyFhIOrKes2GhOfHNbzG6udRYsWYYxh8eLFGGsEbgEZg/X0Aeyn9tK9R/fwn18pP2w2G48/8QS//vWveWV7MjN6l4a8QftiiVZzwe9qG2t4U8sY+PhIPO8cTGTUqFEMHTo0LOfVKwufvn378txzzxJfVUzS1gXEH1yNVISuh8MlLHYqKipYsGABFRUVQZlDqt6MB2vRIZJ2vk/CgVVc0asnL/z2t9rIraLShAmtiNG2AAASUklEQVQTmDVrFhsL7Dz+eRoLv3ZQHsY1hXqlVOFxlvHeO2/RxnqWXin+18AIBo+BDSft/Hxja+buSWLQtdfy05/+FIvFEpbz16vrbFMQrK6zJ0+eZP78+by/cCFulwt3y/Y403vgat0Z4kL3ESYsXWcvIpWl2E7tJb5wH1SepV379ky/5x7Gjh2rYy9U1Nu+fTtz58xh3fr1JNlhXIcyRrWvoHWIxlfU9Pwmbw+kUHaZreZ0w8YCO4sPtyDvbBwd2rdj2j3Tg/Z7GtRxFk1BsMdZFBQUsGTJEhYtXkzByZOINZ7K1l1wpXXzrlgX5E/dYQsLlxNb0dfYCvdjKT2GiHBNTg6Tbr6ZESNGaEioJmfXrl3MmT2btZ9/TpxAv1Qn17WvZECaM2QzyoY6LIyBr0stfHLMwdqTDsqqoGNWB6bdM53rr78+qL+nGhZB4vF42LRpEx988AGrVn2C01kJjmQqU7tRld4jaG0bIQ0L48FScgzbqT3YzxzGeNy0a9+BCeNv4qabbqJNmzbBOY9SEZSXl8fSpUv5YOkSCovO0DIehmeWc137Sjq0CP6UHxD8sCitEtYej+eT4wkcLo3DZrNy3XWjmDhxIldffTVxIZhvTsMiBMrKyli9ejXLPviALzdtwhjjvU2VcQWu1p1AGv4/MhRhIVXl2E7uPH+bqUVSMuNuvIFx48ZxxRVXaJuEapZcLhfr169n6dIlrFmzBrfbQ/dWbka2LWdIGycJQWiQDmZYeAxsO23jk2PxbDoVj8sDPbp35+ZJkxg7dizJyaHtbBPUEdzKKzExkXHjxjFu3DhOnDjBBx98wKJFizm5PxccLalo09c7JXlceBqc6iIVJdiPbyG+cD/GeMjx3WYaNmyYTiComj2r1cqwYcMYNmwYRUVFfPTRRyxetJC/785j3j4YlOGdYLBLkCcYDFSJU/j4iINVxxI5XQEtk5O47ds3MWHCBLp3j77eiHpl0Uhut5tPP/2U+fPfYPfuXZDQirKOg3G3Cmwi3qBcWbirsB/7ivgT27FZ4hg/fjzf/e536dgx4suNKBVRxhh27drFkiVLWP7Rh5RXVNIn1cXEjg2burwxVxbHzsWxLC+B1SccVLlh0KBrmTgxch/m9MoiTCwWC6NHj2bUqFGsX7+e/3rpDxzb8yHOzD5UdhrUqFtTgZCKYpL2LYfyYsaNG8cPfvCDsK+4pVS0EhF69+5N7969mTFjBosXL+btt97kha9Ok5Xk4Xtdz9I/PbRdYE+UxfGv/S34osCO1WblpgnjmTx5ctgWOWosDYsgEREGDx7M3Dmz+Z//+R/efvttxFVGRdfrQz5liJQXk7x7MS0cNv7viy9y9dVXh/R8SjVlycnJ3Hnnndx+++2sWLGC1+fO4XdbjnBNhpOpPc6R7gjuFCJONyw+nMCiQ4lY7fHcPe17fPvb36Z169ZBPU+oaVgEmc1mY9asWbRu3ZrXXnsNd9IOqtr0Dd0JPR5aHFhBC4eNP7/yCllZEV+HSqkmwWazMW7cOK6//nrefPNN5s6ZzU/W2ZnW8ywj21UG5Rz5Zy28tK0VJ8qE66+/npkzZ5KRkRGUY4ebjuAOke9///sMHHgNCce3gCd0DWnWooNI2Wn+44knNCiUagCbzcbUqVOZM/d1+vQbwGs7k3j3YAKNbc7dVWTl2S9TcMW35oUXXuAXv/hFkw0K0LAIGRFh8uQ7MM5yLDUaroPNevogGZmZjBw5MmTnUCoWtG3blt/85rfceOONvHMwkTf3N3x+th1FVn7zVSvS22bxp1f+TE6O3/bjqBfRsBCRjiKyQkR2ish2EXnUtz1VRD4Skb2+f5vWzT2ffv36AWApOxWyc9griujfr19IBusoFWtsNhtPP/00kyZNYvHhBL48Ffia18VO4c87WtG+QxZ//NPLtG3bNgSVhl+k/8K4gMeNMb2BIcBDItIHeAr42BjTA/jY97zJadGiBY6ERMRZ7ndfT2IqxmLDWGy4ktviSUz1fwJjMJXnyMzMDEK1Sinw3hV4+OGH6d6tK6/tasnZqsA6qMzenUSZx8Yv/vOXYVvFLhwiGhbGmGPGmE2+x6XATqADcBswx7fbHOBbkamw8RITExGP/y55lZ2G4E5Mw52YRvkVE6nsNMT/wY0HjEdXtFMqyOLj4/nJ089w1gkf5Dnq/b4DJRa+KLBz97RpdOvWLYQVhl+kryzOE5EuwNXAOqCNMeYYeAMFqPWjs4jMEJGNIrKxoKAgXKUGJD7eHroGbo/Ld4740BxfqRjWrVs3Ro26jg/zE6mo5xToC79OJDmpBbfffntoi4uAqAgLEUkCFgA/NMbUe0ikMeZVY0yOMSYnWnsZOByOel1ZNIT4wsLhqP8nH6VU/U2e/F3KXbD+pP8PZGcqhS8L7Uy65dZmebUf8bAQERveoJhnjHnHt/mEiLTzvd4OOBmp+hqrdUoKca7g9Nm+mLgqAJrVfVGloknfvn3J6tCez05c+IGsU5KLTkkXXm58fiIej4Hx48eHs8SwiXRvKAH+Cuw0xvy+xkvvA/f4Ht8DvBfu2oKlbdu2WJylNLrTdi3ifCv5tWvXLujHVkp5G7vH3nAju85YKXF+09B9V88y7upZdsG+Gwri6d6ta5OZviNQkb6yGA7cDYwRkc2+r4nAr4AbRWQvcKPveZPUo0cPjLMccZ4L+rHjzp3CYrE02x9OpaLByJEjMQa+KKh7kr8zlcK+YivXjRodvsLCLKLTfRhjVgN19UsbG85aQqV///4AWEuOUJXRK6jHtpcepXfv3trArVQIdevWjbaZGWwudHJ9h9pvKW8utGOAESNGhLe4MIr0lUWzl52dTZu2bbGdPhjU40pFMXKusFn/cCoVDUSEYSNGsq3IjrOOjo1fnrLTJjOD7Ozs8BYXRhoWISYijL/pJiwlR5HK0qAd11awBxHhhhtuCNoxlVK1GzJkCFVu2Fl06YjuKg/sKLIzdNjwZr36pIZFGNx8883ExcVhP7kzOAd0u3AU7mX48BGkp6cH55hKqTr179+f+Hg7XxVe2m6x+4yNSjcMHjw4ApWFj4ZFGGRmZjJq1CjiT+0Bt7PRx7MV7sNUVTB58h1BqE4p5U98fDwDBlzN1qJL2we3FtqwWS0MGDAgApWFj4ZFmNx5550YlxP7yV2NO5Dx4DixjV69rjg/UaFSKvQGDRrEiTKhoPzCP5vbzsRz5ZVXkZCQEKHKwkPDIkx69erFwGuuwXFyx/lpOhrCevogVJRw111Tm/X9UaWizcCBAwHYUaPdosQp5JXGcU0zmILcHw2LMLr7rrswzjJsBXsadgBjcBzfQsdOnRk+fHhwi1NKXVaXLl1IaZl8QSP3rjPex7GwlLGGRRgNGDCAvn2vxHFia4MmF7QWHULKiph29126foVSYSYi9BtwNXtKvmm32H3GSny8nZ49e0awsvDQvzhhJCLcd9+9UHkOW8HuwN5sPDiOfUmHrCzGjBkTmgKVUpfVr18/TpXD6Qrvn869JfH06d0Hmy3wRZKaGg2LMBs4cCD9+/f3rs3trv9stNbCA0hZEQ/cfz8WiyWEFSql6nLllVcCsK/ESqUbDpfG0de3rbnTsAgzEWHGjBkYZxn241vr9yaPi4Sjm+jeowejRo0KbYFKqTp1794dm83K/hIrX5da8Rjo06dPpMsKCw2LCOjbty+jR4/GcWIbUnnW7/7249ug8iwPzZypbRVKRZDVaqVbt24cLLFxsMQ7tV6vXsGd8y1a6V+eCPnBD36ARYT4/I2X3U+c53Ac38qIESNioseFUtGuR4+e5JXZOHTWQmpKK9LS0iJdUlhoWERIu3btmDLlTmynDxBXeqLO/eLzN2IRmDlzZhirU0rVpWvXrpxzGradjqdr9+6RLidsNCwiaMqUKbROTSMhfz0YgycxFU9i6vnX484WYCvcz/e+913at28fwUqVUtWq148pdgqdO3eJbDFhpGERQYmJicz4Xw8Qd7YAa9FBKjsNobLTEO+LxuA4spGWLVsxderUyBaqlDqvQ4cO5x/H0oc4DYsIGzduHB07dcZxdPMFS69aSo9jKTnGPfdMa5aLvyvVVNVso2jTpk0EKwkvDYsIs1gsTL9nGlJ+BsuZvPPb7ce3kNK6NZMmTYpgdUqpi1mt3ywwGiuN26BhERVGjRpFWnoG8QXe9S6kogRr8RG+8+1v65KpSkWxlJSUSJcQNhoWUcBqtTLp5olYio8gzjJshfsQESZMmBDp0pRSl5GUlBTpEsJGwyJKjB49GgDrmcPYi/O48qqryMjIiGxRSqnLau5rWNSkYRElunTpQmpaOtbTB5FzhQweNCjSJSml6lA9R1TN9ovmLna+0ygnIgzo34/c3Fzgmx9GpVT0efbZZykqKop0GWGlVxZRpFu3bucfd4+hkaFKNTUpKSlkZ2dHuoyw0rCIIjUH+8RSw5lSKvppWESRzMzMSJeglFK10rCIIrHUZ1sp1bRoWESRFi1aRLoEpZSqlYZFFHE4HJEuQSmlahW1YSEi40Vkt4jsE5GnIl1PONjtdgBuvvnmCFeilFIXispxFiJiAf4E3AjkAxtE5H1jzI7IVhZaIsJ7772nt6OUUlEnWq8sBgH7jDEHjDFO4A3gtgjXFBatWrWKqVGhSqmmIVrDogOQV+N5vm+bUkqpCIjWsJBatplLdhKZISIbRWRjQUFBGMpSSqnYFK1hkQ90rPE8Czh68U7GmFeNMTnGmBydoVUppUInWsNiA9BDRLJFxA7cCbwf4ZqUUipmRWVLqjHGJSKzgA8AC/A3Y8z2CJellFIxKyrDAsAYswRYEuk6lFJKRe9tKKWUUlFEjLmkk1GTJCIFwKFI19GMpAOnIl2EUrXQn83g6myM8dtDqNmEhQouEdlojMmJdB1KXUx/NiNDb0MppZTyS8NCKaWUXxoWqi6vRroApeqgP5sRoG0WSiml/NIrC6WUUn5pWKgLxOKiU6ppEJG/ichJEdkW6VpikYaFOq/GolMTgD7AFBHpE9mqlDpvNjA+0kXEKg0LVVPMLjqlop8x5hPgdKTriFUaFqomXXRKKVUrDQtVU70WnVJKxR4NC1VTvRadUkrFHg0LVZMuOqWUqpWGhTrPGOMCqhed2gm8qYtOqWghIvOBtUAvEckXkfsjXVMs0RHcSiml/NIrC6WUUn5pWCillPJLw0IppZRfGhZKKaX80rBQSinll4aFUhcRkdki8qyIjBSR3fV8z3QRWR3q2pSKFA0LpepgjPnUGNMr1OfRoFFNgYaFUkopvzQsVMwTkatFZJOIlIrIvwCHb/toEcmvsd9TIrLft98OEfn2pYeS/xaRYhHZJSJja7zQSkT+KiLHROSI7zaXRUR6A38GhorIWRE549s/XkReEJHDInJCRP4sIgm+19JFZJGInBGR0yLyqYjo77IKKf0BUzHNNwfWu8DrQCrwFnB7HbvvB0YCrYBfAv8QkXY1Xh8MHADSgV8A74hIqu+1OYAL6A5cDYwDHjDG7AT+N7DWGJNkjEnx7f9roCcwwPeeDsDPfa89jnfSxwygDfA0OjuwCjENCxXrhgA24L+MMVXGmLfxTqh4CWPMW8aYo8YYjzHmX8BevAtGVTtZ4zj/AnYDN4tIG7yrD/7QGHPOGHMSeBHvRI2XEBEB/hfwI2PMaWNMKfB8jf2rgHZAZ9+5PjU6b48KMWukC1AqwtoDRy76Y3uoth1FZBrwGNDFtykJ71VEtdqO0x7ojDeQjnlzAPB+UKu50FRNGUAi8EWN/QWw+B7/FvhP4EPf668aY35V1zeoVDDolYWKdceADlLjrzLQ6eKdRKQz8BreWXnTfLeLtnHhglG1Heco3lCoBNKNMSm+r5bGmL6+/S6+KjgFlAN9a+zfyhiTBGCMKTXGPG6M6QrcAjxWs31EqVDQsFCxbi3etoRHRMQqIt/hwltL1Vrg/aNeACAi9wJXXrRPpu84NhGZDPQGlhhjjgEfAr8TkZYiEici3URklO99J4AsX/sJxhgP3mB6UUQyfefrICI3+R5PEpHuvmAqAdy+L6VCRsNCxTRjjBP4DjAdKAK+B7xTy347gN/hDZcTwFXAZxfttg7ogffK4DngDmNMoe+1aYAd2OE7z9t42x0AcoHtwHEROeXb9mNgH/C5iJQAy4HqMR89fM/P+up52RizsiHfv1L1petZKKWU8kuvLJRSSvmlYaGUUsovDQullFJ+aVgopZTyS8NCKaWUXxoWSiml/NKwUEop5ZeGhVJKKb80LJRSSvn1/wEKGCn8EIY2EgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb67c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='SkinThickness', data=diabetes, hue=\"Outcome\")\n",
    "plt.xlabel('diabetes', fontsize=12)\n",
    "plt.ylabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过这种方法可以探索所有特征的分布特征，以及与结果变量的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203eb759518>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tahM+/HAS777zIRMnTMn2PlyJK+l/mTKl8PHxAaB06ZJUqnQD+/cdBKBIkZRJC6VKlaB9+zb89OO07OrSFTkREkqucoH4ly6K8fOlYLtbORq0Jl0d36IFU28XaNmAU7tS+mz8fLnh08Ec/nUhR2auyNa4M8vOkJ2UKFeSYqWL4evnS9N2TVkdtDpdnRuq30D/N/ozstcIjsZd/eu85MroKmRZbxXwoTGmgrV2lzEmL1AK2A7cYIwpa60NAy5njUkBIN5ae8IYU4WU7M6lzAeeBN43xvgA11hrj6UpnwOMNMZ8Y609bowpCSSS8to4bK392rNepvvldTd7PT/sTYI3bOLIkWPc3qELfXt15f52OWNR5uVwu92889L7jPv2XVw+LqZ/P4s9O8N4/PmebAvZztK5K6haqwpvfz6S/Nddy60tG/H4cz3odFt3ylYsw+C3nsMmJ2NcLqZ8+E26q5flFMnuZMa9PJ63v3kDl8vF7B/mELZzHz2e68aOkJ2sCFpJ5VqVGPnZcPIVyEfDlrfQY+Cj9Lj9MRbPXEqdxrWZNO9TrLUELwpm5bxVl35Qh7ndbl4Y9Cq//P4FPj4+fPPVT2zfFsrgoc+wcf2fzJ41n68m/8iEz95jXch84uOP0Kv7AAAaNa7P4KEDcCcl4XYnM+iZVzgSf5QiRQvz7Y8TyZXLH5ePD0sXr2TSZ9863NPz+6/3H1Keg0EDX2HqtCn4+PgwZcqPbNsWytCXn2X9+s3MmjmPyV/+yGefj2bT5kXExx+h26NPXXK/X345jlub3kLhwgXZGbqS114bw5TJP2ZDj/6ZK+l/o0b1GTjoSZKSkkhOTmbAgJeJi4sH4JtvP6ZQoYIkJSYx8NmXOXLk2MXCuHq4kzn48ieU/2o4xsdF3A/zObXzAMUHPsKJzbs4FrSGIj3upkDLBpDkJunIcfYNSlkDdd3djcnXoDo+111LoY4tANg/aBwnt+aM7BOkfA5MePljRnw1EpePi6Afgti/cz+dB3YhdHMoa4JW0/OlXuTOm5v/fTwYgJiIGEb2SrmM+ls/v0Wp8qXJfU1uvlw9mXHPj2X9kvVOdin7eOkFfszVfBWWq50x5ri1Nt8528oCMzzrRs5sawG8BeTybBpqrZ1mjGkHvAPEAmuAYtbazsaY4cBxa+27nvZ/AncDkcDvQElgBynrZoZbaxeljcUY0xG421rb3bOY/xPgBlIyM09aa1eeU/8Z4My1do8DXYAKntiSSRnQPGmtveCKuMTYPf/pF1Ljmj2cDsFR17j8nQ7BcRuP5JwvA5I1EpI1ZeW/bllALadDcNTL6BiYsX/mpWbYZKvTW+Zn+fezXNVvz/Y+awDjIGNMPk/WwwAfAqHW2jFOx/VvaACjAcx/nQYwogGMaACjY+CqG8D8GZT1A5gbW2Z7n7UGxlmPeRb2byFlethEh+MREREREbmqaQ2MgzzZlhyZcRERERGRq5yXroFRBkZERERERHIMZWBERERERLyQtTnkx1r/IWVgREREREQkx1AGRkRERETEG1mtgREREREREXGUMjAiIiIiIt5IVyETERERERFxljIwIiIiIiLeyEvXwGgAIyIiIiLijZJ1GWURERERERFHKQMjIiIiIuKNvHQKmTIwIiIiIiKSYygDIyIiIiLijXQZZREREREREWcpAyMiIiIi4o20BkZERERERMRZysCIiIiIiHgjrYERERERERFxljIwIiIiIiLeSBkYERERERERZykDI5micc0eTofgqOWbvnA6BEc9XHeA0yE4rkHBik6H4KjHkgo5HYLjfvL/2+kQHLX9dLTTITjuc1dup0Nw1Aj3KadDkHNY63Y6hCyhDIyIiIiIiOQYysCIiIiIiHgjrYERERERERFxljIwIiIiIiLeyCoDIyIiIiIi4ihlYEREREREvJHWwIiIiIiIiDhLGRgREREREW/kpWtgNIAREREREfFGmkImIiIiIiLiLGVgRERERES8kZdOIVMGRkREREREcgxlYEREREREvJHWwIiIiIiIiDhLGRgREREREW+kDIyIiIiIiIizlIEREREREfFGugqZiIiIiIiIs5SBERERERHxRloDIyIiIiIi4ixlYEREREREvJHWwIiIiIiIiDhLGRi56t3SvAGDRj6Fy+Vi6nczmTL+23TldW6uybMjnqJC1RsY+uQIFsxcnFq28sACdm/fA0BUeDTPdR+SrbFntaGjRrNk+RoKFbyO37+e4HQ4WaZ2s5voMaw3Lh8f5n8/l98//iVd+d297+H2Ti1JTkrm2OGjfPj8OGLDYwgoWYTnJw7G5XLh6+fL7C9nMPebPxzqReao17wuTwx/Ah8fF7O/+4MfP/opXfl9j91Lm05tcLvdHI07yujnxhAdHu1QtJmn2G01qT2iK8bHxd5vF7Fj/PTz1ivZtgENP3uG+W2GEh+yl4K1b6DuO71TCg1sfe9XImavzb7AM0mtZnXoPqw3Lh8XC74PYurHv6Yrb9u7PS06tcSd5ObY4WNMeP4DYsNjUsvz5MvD6PnjWTNnFV+88ml2h58pGt12My+OHIDLx4ffvpnOpPFfpSu/6ZbavDDiGSpWK8+LTwxj3oyF6cqvyZeX35d+x4LZi3ljyOjsDD1TVG1Wi46vdMfl42LFDwsI+nhquvIWvdrSsFMLkpPcHD98jK9fmEB8eCwA9/zvEarfdhMAf3zwC+tnrMz2+K9U/uZ1KDX8MfBxEfddEIc+Sv85UOiBFpR8qTuJUXEAxHw5i7jvgwAo/9UwrqlTib+Dt7G7x2vZHrujvHQNjAYwVxljTDFgDHALEA8kAG97bj9nrb3bwfCyncvl4oVRA+jfaRDRkTFMnjWRpXOWszd0X2qdqPBoRgx4gy5PdMrQ/vSp03Rp2Ts7Q85WHe5qySP3t2fIyHedDiXLuFwueo/sw4jOr3A4Ko43p73H2nlrOBh6ILXO3i17ePHugSScSqBVlzvpOrg7Y/q/w5HoeF667wWSEpLInTc3o+d+QHDQGuKjDzvYo3/P5XLR77V+DH5kCLGRsXwwYyyrglazP3R/ap3df+7mqbZPc/rUae7u2pbeL/VkVN83HYw6E7gMdUZ1Z+lDb3Ai8jC3zx5JxNz1/LUzPF0132tyU6F3a+LW7UrddmzHQea3GYp1J5O76HXcMX8UkXPXY90550PduFz0HNmH1zsPIy4qjjemvcPaeWsIDz2YWidsyx4G3z2IhFMJtOzShs6DuzG2/9n3hQcHPcLW1VucCD9TuFwuhrzxHH0efIZDkdF8+8fnLJq7lD07w1LrRIVH8fIzr9Gt7yPn3Ue/Fx9n7coN2RRx5jIuw4MjejK+y+sciYrj+WlvsDloLVG7zh4DB7aGsbTdYBJPJdCkS0s6DO7MF/3HUv22OpSuXo4373oBX38/BvwwjK2LNnLq+EkHe/QPuVyUfq0PoY8MIzEyjsoz3uVo0BpOpfkcAIifvoyDL3+SoXn0hN9w5clFQOfW2RWxZDFNIbuKGGMM8DuwxFp7g7W2LtAJKOVsZM6pXqcqB8PCidgfSVJiEnOnLqBp6ybp6kQejGLXtj0ke+lZhoupV7sGBfJf63QYWapC7YpEhUUSfeAQSYlJLJ++lPotb05XZ8vKzSScSgAgdMMOCgcGAJCUmERSQhIAvv5+GFfOfsurXLsSEWERRO2PIikxiUXTFtOw1S3p6oSs3MTpU6cB2LZ+OwHFA5wINVMVqlOe42GH+Ht/DDbRzYGpqyjRum6GetVf7MjOD2eQfDohdZv7ZELqYMWVyw9stoWdaSrUrsghzzHgTkxixfRl5zkG/jznGCicWlbuxvJcF3Adm5ZszNa4M9ONdapxYO9BwvdHkJSYxB+/z6N561vT1Yk4EEXott3n/SyoWrMyhYsUYuXiNdkVcqYqW7sCsfsOEXcgGneim/XTV1CzVf10dUJXbiHR8xoI2xDKdcVTXgPFK5YidPU2kt3JJJw8zcFt+6jarFa29+FKXFO7IqfDokjYfwibmET8tKUUaNXgstv/tXwT7pw0YMtMNjnr/xyQsz/NvU8LIMFamzoXyFq7z1r7QdpKxpjhxpjn0tz/0xhT1nP7UWPMJmNMiDHmK8+2MsaY+Z7t840x13u2P+BpG2KMWeLZ5mOMeccYE+yp3yfLe30RRYoHcCji7PSX6MgYigRe/hcy/1z+TJ49kc+nf0SzNk0u3UCuOoWKFyY2Mjb1flxkLIWKF75g/RYPtWTDonWp9wsHBvDeH+OYuGoSUyf8kmOzLwCFiwcQE3F2WlBsZCwBF3ku2nRqRfCinDdd6lx5ihfiZHhc6v2TkYfJU7xgujrX3ViGPCUKEzkv4xn2QnXK03LRW7Ra+CbrX5yUo7IvAIWKFyIu3TEQR8HihS5Y/7aH7mDjovUAGGPoOrQHX4+anOVxZqWigUWIijiUej86MoZigUUuq60xhkHDn2L0iPFZFV6WK1CsEPERZ4+B+Mg4ChQreMH6DR+8ja2LUgas4dv2Ua15bfxy+3NNwWup1LA6Bf/B5+jVwK94YRIizh4DiZFx+J3nva/gnQ2pOncs5Sa8iF8O66P8M5pCdnWpDqz/t42NMdWBl4DG1tpYY8yZT7jxwBRr7WRjTE9gHNABeAVoba0NN8Zc56nbCzhqra1vjMkFLDfGzLXW7j3P4z0OPA5QpkBFiuYN/LehX6xPGTf+gzOo7es/SOyhOEpcH8hHP41h17Y9hO+LyLwAJcsZMr4GrD3/i+DWe5tTvkYFXnlocOq2uMhYBrV5moJFC/HCp0NYOWsFR2OPZFm8Wem8h8MFjocW995GxZqVeP6BF7I2qOxwnn6n67gx1Hq1C8HPTDxv88MbdhPU/EWurViC+mOfIGpBCMmnE7Mm1ixwvmPgQu+DTe5tRvkaFRj+0EsAtHr0TjYuXJduAJQTnf+1f3kfBg/1uI9l81emOxmW0/yTz8L6HZpwfc3yjH1oOADbl26iTM3yDPp1JMfjjrF3fSjJbnfWBZsVLvUeABwNCiZ+6hJsQhIBXdpQdswzhHZ6OXviu5p56ewUDWCuYsaYD4EmpKyDef4ymrQAfrbWxgJYa8+cam4I3Oe5/RUpa2oAlgNfGmN+BM6sCG0F1DTGdPTcLwBUBDIMYKy1nwCfADQo0SxLJmZER8ZQrETR1PtFA4sQE3X5H8Sxh1LOWEXsj2T9io1UvrGiBjA5TFxULAFpzqQVDgwg/lDGLEqNxrW4v/8DvPLgkNRpY2nFRx/mwM79VG1QjVWzVmRpzFklNjKWIiXOnnUOCAwg7lBchnp1mtTm4ac68dwDL5CYkHO+qF/IycjD5Cl59mxrnsBCnDx0dhDqmy83+auUptmvQwHIXaQAjb4cxIru7xEfcvat66/QCJJOnKZAlVLptl/t4qLiUqdFAhQOLHyBY6Am9/XvyPAHh6YeA5VuqkyV+tVo2fVOcl+TG18/X079fYrv3voqQ/ur2aGIGIqXKJZ6v2hgEaIv87OgZt0buenmWjzY/T7y5s2Dn78fJ/4+ydjXP86qcDPdkag4CpY4ewwUDCzM0ej4DPUqN65B6/738f5Dw9O9D8758DfmfPgbAN3HPkX03sisDzoTJUbG4V/i7DHgF1iYxHOOAfeRv1Jvx347l5KDH822+CT7aQrZ1WULcNOZO9bafsDtwLl58iTS/9/l9vxruLz8hPXs/wlgKFAa2GiMKezZx1PW2tqev3LW2rn/pjOZYevG7ZQuV4oSpYvj6+dLq3tasHTu8stqe22BfPj5+wFQoFABatavwd40Cz4lZ9gVEkpguRIULV0MXz9fGre7leCg1enqlKt+A33e6MubvV7jWNzR1O2FihfGP5c/ANfkv4Yq9aoSsTv9wu+cZEfITkqWLUExz3PRvH0zVgWtSlenfPXyPP3m0wzr+SpH0zwXOVn8xj3kK1ecvKWLYPx8KH3PLUTOOTtNMOmvk0yv/gSzGwxgdoMBHF6/K3Xwkrd0EYxPyttl3lIBXFs+kL8PxFzooa5Ku0NCKV4ukCKli+Lj50ujdk1YG5R+LUfZ6uXo/UZf3u41Kt0x8MEzY+jX6DGeavI4X7/+JUt+XZjjBi8AWzZu4/obSlHy+kB8/Xxp0+EOFs9ddllth/R7lTbzb6laAAAgAElEQVT17uOu+vczesR4Zvw0O0cNXgD2heymSNniFC5VBB8/H25q14hNQemnh5aqXpZOo3ozsffbHI87lrrduAzXXJcPgBJVrqdElTJsX7opW+O/Un+HhJKrbCD+pYti/Hwp2P5Wjp5zDPgWPTulrkCrBpzadfDc3fw3JSdn/Z8DlIG5uiwARhljnrTWnnl3zXueemHA3QDGmJuAcp7t84HfjDFjrLVxxphCnizMClIuBvAV0BlY5mlb3lq7GlhtjGlHykBmDvCkMWaBtTbRGFMJCLfW/p0VHb4Ut9vNOy+9z7hv38Xl42L697PYszOMx5/vybaQ7Sydu4Kqtarw9ucjyX/dtdzashGPP9eDTrd1p2zFMgx+6zlscjLG5WLKh9+ku3qZN3h+2JsEb9jEkSPHuL1DF/r26sr97bzrKivJ7mQ+e2UiQ6cMT7mE7I/zOBh6gIcGPsLuTbtYO28NXYd0J3fePAz66EUAYiNieKv365SqUJpuQ3tircUYw7RPfmf/jpz7Gkh2J/Phyx8z6uvXcPn4MPeHuezbuZ9HB3Vl56adrApazWMv9SJP3twMnZByyfDoiBiG93zV4civjHUns3HIl9z63YsYHxdh3y/m2M5wqj1/P/Ehe4mce+GZtwE3V6Zy/3bYRDfWJrNh8BckHD6ejdFfuWR3MpNe+ZQhU4bh8vFhkecYeGDgw+zZtIt184LpMqQ7ufPm5tmPUqYMxkbE8E7vUQ5HnnncbjdvDBnNx9+NweXjw+/fzWD3jr30faE3WzZuZ/HcZVSvXZUxk94g/3XX0qxlE/o+34v7mnVxOvRMkexO5sdXJtFvyhCMj4tVPy4iKvQgbZ99gP2b97B53jo6DO5Crry56fXRswDEh8cy8bF38PHzZcBPKe8Bp46fZPKzH5Ccw9aB4U7mwMufUOHr4RgfF3E/zOfUzgMEDnqEE5t2cTRoDUV73E2Blg2wbjfuI8cJGzg2tXmlX0aRq3wpfK7JzY1rPmff8+P5a3HOvCKdpDCXO4dUsocxJpCUyyjfDMQAfwMTgEN4LqNsjMkDTAWKAsGkTDO701obZozpRsp0MzewwVrb3bPAfxIQ4NlnD2vtfmPMr6RMDzOkDH4GeG6/BrTz3I4BOlhrL3oqN6umkOUUyzd94XQIjnq47gCnQ3DcX8k5f6rWlXgs6cKLyv8rfvJ35DzPVWP76Zy7xiSz3Jq7tNMhOKqn+5TTITjupgNTz7dixzEnf3g1y7+f5XloWLb3WRmYq4y1NpKUbMn5LPLUOUnKWpXztZ8MTD5nWxgp62POrXvfudtImV42xPMnIiIiIjmVly7i1xoYERERERHJMZSBERERERHxRsrAiIiIiIiIOEsZGBERERERb2SVgREREREREXGUMjAiIiIiIt5Ia2BEREREREScpQyMiIiIiIg38tIfrFcGRkREREREsowxpo0xZocxZpcx5n/nKb/eGLPQGLPBGLPJGHPXxfanDIyIiIiIiDe6CtbAGGN8gA+BlsBBINgYM81auzVNtaHAj9baj40x1YBZQNkL7VMZGBERERERySoNgF3W2j3W2gTge+Cec+pYIL/ndgEg4mI7VAZGRERERMQbXQUZGKAkcCDN/YPAzefUGQ7MNcY8BVwD3HGxHSoDIyIiIiIi/4ox5nFjzNo0f4+fW+U8zc69usDDwJfW2lLAXcBXxpgLjlOUgRERERER8UY26zMw1tpPgE8uUuUgUDrN/VJknCLWC2jj2d9KY0xuIACIPt8OlYEREREREZGsEgxUNMaUM8b4A52AaefU2Q/cDmCMqQrkBmIutENlYEREREREvJBNdv53YKy1ScaY/sAcwAeYZK3dYowZAay11k4DBgGfGmOeJWV6WXdrL/wjNhrAiIiIiIhIlrHWziLl0shpt72S5vZWoPHl7k8DGBERERERb3R1XIUs02kNjIiIiIiI5BjKwIiIiIiIeKNsuAqZEzSAERERERHxRlfBIv6soAGMZIprXP5Oh+Coh+sOcDoER3237n2nQ3Bc2zp9nQ7BUY/9tdrpEBzXOaCu0yE46pfD+5wOwXGtS5RxOgRHDUg+7nQIjlvidAD/ERrAiIiIiIh4Iy3iFxERERERcZYyMCIiIiIi3kgZGBEREREREWcpAyMiIiIi4o2sd16FTBkYERERERHJMZSBERERERHxRloDIyIiIiIi4ixlYEREREREvFGy1sCIiIiIiIg4ShkYERERERFvZLUGRkRERERExFHKwIiIiIiIeCOtgREREREREXGWMjAiIiIiIl7I6ndgREREREREnKUMjIiIiIiIN9IaGBEREREREWcpAyMiIiIi4o289HdgNIAREREREfFGmkImIiIiIiLiLGVgRERERES8kS6jLOKM+s3rMXnxJL5e9iUP93soQ3nNm2swcfZHzAv7g6Ztb01X1uel3nwx/1O+XPg5T43om10hZ6razW5i7IKP+GDxRDo8eX+G8rt738OYeeN5749xDPt2JAEliwAQULIIb80YzTuz3mdM0HhadW6T3aFni6GjRtO0bSc6dHnC6VCyTL3mdfl80Wd8sXQSD/V9MEN5jZtv5MNZ45m9dya33tUkXVmvwT35ZN4EPpk3gWbtmmZXyFfs9juasmb9XNaFzGfAwD4Zyv39/fl88ljWhcwnaOHPlL6+JAClry9JRMyfLFkxjSUrpjF67IjUNn5+foz54DWCNwSxev0c2t3TOtv6c6WqNqvFy/PHMGzRWFo+eU+G8ha92vJS0HsMnv02T30zlIIlA1LL7vlfZ16a+y5D542m47Du2Rj1lWndqjlb/lzC9q3LeOH5fhnK/f39+fabj9m+dRkrlk2nTJlSABQqVJB5c3/iyOGdjH3/tXRt5gf9xJY/l7A2eC5rg+dSpEjhbOlLZqjUrBbPz3+PFxaNofmT7TOU39L5Dp794y0GzHqDJ38aRtEKKcdE3uvy0ee7oYzc8gX3vNo9m6POPA2a1+frJV/y7bIpdO7XKUN5rZtr8NkfE1iwby7N2p59r6vTqDafz52Y+he0ezZNWjfOztAlCygDcw5jjBvYDBjADfS31q4wxpQFZlhrb8yEx1gEPGetXWuMCQP+ApKBQ8Cj1tqoK30Mb+FyuXjmtad4/pEXiYmMZcLM8ayYu5J9oftT6xwKj+atge/wUJ8H0rWtXrcaN9a7kV4tU778jPttDLUa1iRk5aZs7cOVcLlc9B7ZhxGdX+FwVBxvTnuPtfPWcDD0QGqdvVv28OLdA0k4lUCrLnfSdXB3xvR/hyPR8bx03wskJSSRO29uRs/9gOCgNcRHH3awR5mvw10teeT+9gwZ+a7ToWQJl8tF/9f68b9HhhAbGcsHM8axMmgV+9McA9HhMbw78D069kk/wG3QogEVb6zAE6374u/vx7s/v0PwwrWcOH4iu7vxj7hcLt4ZPZx723cjIjyKBUt+Zfas+ezYviu1TtduD3D0yFHq1rqd+zq2ZfjIF+jV7RkAwvbup2mjjF/wBr3Ql9iYOOrXaYkxhoKFrsu2Pl0J4zI8OKIn47u8zpGoOJ6f9gabg9YStSs8tc6BrWEsbTeYxFMJNOnSkg6DO/NF/7GUu6kSN9SrzKg2zwMw8OcRVLylGqGrtjrVncvicrkYN/Z12tz1MAcPRrJq5Symz5jLtm2hqXV69niY+PijVKnWhAcfbM8bo17ikc5PcurUKYYNf5vq1atQvXrlDPt+9NH+rFufcz4HIOU1cO+IHnzaZRRHo+J4atrrbA1aR3Sa18CGqctZ9c08AKrdUZd2L3fl825vkng6kTnv/UTxyqUpVqmUU124Ii6Xi2dff5qBD79ATGQMn8z6iGVzV7IvdF9qnUPh0Yx69m06PZH+u8CGFRvp1Srle8C1113Ld8umELx4bbbG7yitgfnPOGmtrW2trQUMBt7Ihse8zfN4a4Eh5xYaY3yyIYZsf6zLUaV2ZSLCIojcH0VSYhILpi6icatG6eocOniIPdv2knzOQWqtxT+XH77+vvj5++Hr60t8zJHsDP+KVahdkaiwSKIPHCIpMYnl05dSv+XN6epsWbmZhFMJAIRu2EHhwJQzr0mJSSQlJAHg6++HcXnn4V6vdg0K5L/W6TCyTOXalYkIiyTKcwwsnraYRq0apqtz6OAh9m7fi7Xpj4EyFa9n0+rNJLuTOXXyNHu27qVe87rZGf6/UrdeLfbs2ce+sAMkJiby688zuavtHenq3Nn2Dr775jcApv72B82aNzzfrtLp0rUjY96dAKS8PxyOi8/84LNA2doViN13iLgD0bgT3ayfvoKareqnqxO6cguJnveBsA2hXFf8TGbB4pfLD18/X3z9/fDx9eFYzNFs7sE/16B+HXbvDmPv3v0kJiby449Tad8ufcasfbtWfPXVTwD88stMWtyWkn08ceIky1cEc+rU6WyPO6uUrl2B2H1RHPa8BkKmr6R6q3rp6pw+fjL1tn/eXKnvB4knTxO2dgeJpxOyNebMVLVOFcLDwoncH0lSYhLzpy6kSev03wWiDh5iz7Y92It8YW/etimrF67htBe9Nv6rvPMbTebJD2T4hDPG5DbGfGGM2WyM2WCMue0S2/MYY743xmwyxvwA5LnA4y0BKnjaHDfGjDDGrAYaGmPqGmMWG2PWGWPmGGMCPfWeNsZs9ez7e8+2ZsaYjZ6/DcaYa40xzY0xM9L0Ybwxprvndpgx5hVjzDLgAWNMeWPMH57HWmqMqZJJz+c/FhAYQHRkTOr9mKhYAgIDLtLirK3rt7FhRQi/rPuBn9f/QPDitezftf/SDa8ihYoXJjYyNvV+XGQshYpfeMpDi4dasmHRutT7hQMDeO+PcUxcNYmpE37xuuzLf0FA8cLERKQ5BiJjKXyR10Bae7btoX7zeuTKnYv8BfNTq2FNipQoklWhZprAEsUIPxiZej8iPIrAEsXS1SmRpo7b7ebY0eMUKlwQgOvLlGLx8mnM+ONbGjZK+ZKXv0DKIHfIy8+yaNlUvvjqA4oUzRnThwoUK0R8RFzq/fjIOAoUK3jB+g0fvI2tizYCsHd9KKErt/B68ERGrZnItiUhHNodfsG2V4sSJYtz4GBE6v2D4ZGUKFH8gnXcbjdHjx6jcOELPy9nfPbZaNYGz+WlIQMyN+gsVKBYQY6meQ0cjYwj/3leAw27tuTFxe9z1/8eYdrwydkZYpYKKB5AdLr3wRiKFL+87wJp3X7PbcybujAzQ7v62eSs/3OABjAZ5fF88d8OfAaMPE+dfgDW2hrAw8BkY0zui2x/Ejhhra0JvA5c6BTo3aRMXwO4BvjTWnszsBr4AOhora0LTPLsB+B/QB3Pvs8sAngO6GetrQ3cCpw9LXNhp6y1Tay13wOfAE95Hus54KPLaJ8lDCbDtnPPMl9IibIlKFPxeh6o/zAP1OtEnca1qXlzjcwOMUv9k/7fem9zyteowNSJv6Zui4uMZVCbp+nftA/N7m9BgYCcMWVG0jD//hhYt2Q9axYG8/7voxky/n9sW78Nd5I7syPMdOZy+nyBOoeiYqhRtSnNGrfnpf+9zqeTxnDttfnw9fWlZKlAVq9aR/Mm9xC8egMjXx+cVV3IVOd7PrjAS6B+hyZcX7M88z+ZBkBAmWIUq1CSobc8yUu3PEGlRjdSvkHVLIw2c1zOa+D8dS6+367dnqLOTXfQ/LZ7adK4AV26dLyiOLPNZb4GVn4VxFvNBjDrzW9p8dS9WR9XNjlv9y/zffCMwkULcUOVcqxZFJxJUYmTNIDJ6MwUsipAG2CKyfgu2QT4CsBaux3YB1S6yPamwNee7ZuAcyffLjTGbCQl43Nmypob+MVzuzJwIxDkqTcUODORdRPwjTGmC5Dk2bYcGG2MeRq4zlp7ZvvF/ABgjMkHNAJ+8jzWRCDwfA2MMY8bY9YaY9ZG/H3wMh7in4uJjKFo4NkzxkWKBxAXFXeRFmfd2qYxW9dv49SJU5w6cYo1C4OpdtPV/8GdVtw5GafCgQHEH8qYRanRuBb393+AN3u/ljptLK346MMc2Lmfqg2qZWm8kvliI2PTZU2KBAZw+DyvgQv57oPvebJNP/7XeQgYQ/jeiEs3clhEeBQlS5192ylRsjhRkdEXrOPj40P+AvmIP3yEhIQE4g+nTBUN2biFvXv3U75CWQ7HxfP33yeYMW0uAFN/m03N2tWzqUdX5khUHAVLnM0WFQwszNHojNPfKjeuQev+9zGx99up7wO1WjcgbEMoCSdOk3DiNFsWbaRcnYrZFvu/FX4wktKlSqTeL1UykMjIQxes4+PjQ4EC+Tl8+OLTAiMiUpaYHj/+N999/zv169XO5MizxtGowxRI8xooEFiYY+d5DZwRMn0l1VvWu2B5ThMTGUvRdO+DRYg9dHnfBc64rV1zlsxeliNO4mSqZJv1fw7QAOYirLUrgQDg3DkX5zkXcNHtcMHzZUDKGpja1tpHrbVnFmmcstaeOcoMsMVTp7a1toa1tpWnrC3wISlZnXXGGF9r7ZtAb1Kmqq3yTAFLIv3/d+5zYvjb868LOJLmsWpba8/7rd9a+4m1tp61tl6Ja7JmYeD2kB2ULFeS4qWL4+vnS4t7mrMiaOVltY0Oj6bWLTVx+bjw8fWh1i010y3+zwl2hYQSWK4ERUsXw9fPl8btbiU4aHW6OuWq30CfN/ryZq/XOBZ3dm57oeKF8c/lD8A1+a+hSr2qROSAqSOS3o6QHZQsW4LintdAs/bNWBm06rLaulwurr0uZepUuSrluKFqOdYtWXeJVs5bv24T5cuX4foypfDz8+O+jm2ZPWt+ujp/zJrPw51TzjDfc28blixOeU4KBxTC5VnvVaZsaW4oX4awsJSLXsyZvYAmTVPWkDVt3jDdRQGuZvtCdlOkbHEKlyqCj58PN7VrxKag9IuQS1UvS6dRvZnY+22Oxx1L3R4fEUuFm6vh8nHh8vWh4s1VidqVNSecMlPw2o1UqFCOsmVL4+fnx4MP3sP0GXPT1Zk+Yy5du6Ys2L7//rYsXLT8ovv08fFJnWLm6+tL27Z3sGXLjqzpQCY7GLKbgLLFKeh5DdRq15CtQemP5YCyZ6fYVWlRh7gw77ke0PaN2ylVriSBnu8Ct99zG8vnrvhH+7i9w23M/69NH/NiugrZRXi++PsAcUDeNEVLgM7AAmNMJeB6YMdlbF9ojLkRqPkPQ9kBFDHGNLTWrjTG+JGS2dkGlLbWLvSsX3kEyGeMKWyt3QxsNsY0BKoA64BqxphcpAxebgeWnftA1tpjxpi9xpgHrLU/ebJPNa21If8w5kyR7E5m3MvjefubN3C5XMz+YQ5hO/fR47lu7AjZyYqglVSuVYmRnw0nX4F8NGx5Cz0GPkqP2x9j8cyl1Glcm0nzPsVaS/CiYFbOu7wvfleLZHcyn70ykaFThuPycbHgx3kcDD3AQwMfYfemXaydt4auQ7qTO28eBn30IgCxETG81ft1SlUoTbehPbHWYoxh2ie/s3/Hvks8Ys7z/LA3Cd6wiSNHjnF7hy707dWV+9vlnMvjXkqyO5nxL3/EqK9fx+XjYs4Pc9m3cx+PDurKzk2hrApaRaValRj26ctcW+BabrnjZroO7Mrjd/TBx8+H0b+kXJ3txPETvPn02yS7r/7fBHC73bww6FV++f0LfHx8+Oarn9i+LZTBQ59h4/o/mT1rPl9N/pEJn73HupD5xMcfoVf3lPUMjRrXZ/DQAbiTknC7kxn0zCsciU8Z2A9/+W0mfPYub7w1lNjYw/R/4kUnu3nZkt3J/PjKJPpNGYLxcbHqx0VEhR6k7bMPsH/zHjbPW0eHwV3IlTc3vT56FoD48FgmPvYOG2atolKjGxky512stWxbvJE/5693uEeX5na7eWbAUGbN/BYfl4svJ//A1q07GT7sOdauC2HGjCAmffE9k78cx/aty4iPP8IjXc5eKn/XzlXkz58Pf39/7mnfhjvbPsy+fQeZNfNb/Px88fHxYf78pXz2+TcO9vLyJbuTmfrKl/SeMhiXj4vgHxdxKPQgrZ7tyMHNe9k6bx2NurWiQuMaJCclcfLo3/ww6OPU9v9bNo7c+fLg4+dL9Vb1+KzrG+muYHa1c7uTeX/oB7z77Vu4XC5m/TCbsJ376Plcd3aE7GB50Eqq1KrMa5+/yrUF8tGoZUN6DupGtxa9ACheqhhFA4uycaUjX2UcZb30d2DMP51D6O3SXEYZUjIfQ6y1M9NeRtmzrmUCKVmPJGCgZxBxoe15gC+AasBGUhbqP53mMsr1rLVnV2qnxHHcWpsvzf3awDigACkDz/eBL4GFnm0G+Npa+6Yx5gPgNlKmoW0FultrTxtj3gbuAUKBBGCatfbLc2MwxpQDPiZl6pgf8L219uyPKZzHbaVa/qdfSIV9LnRdhv+G79a973QIjmtbJ2f+zlBmCY7PGdmMrNQ54Oq/wltWmhCR4ZzYf87AEjnnt5aywqrE6EtX8nJLwudfbDZOtjs++P4s/36W741fsr3PysCcw1p73ssIW2vDSFmHgrX2FND9PHUutP0kkPFXl1LKyl5ge75z7m8kZS3NuZqcu8Fa+9QF9vkC8MKlYrDW7iVl/Y+IiIiI5FT6HRgRERERERFnKQMjIiIiIuKNlIERERERERFxljIwIiIiIiLeyHrnVciUgRERERERkRxDGRgREREREW+kNTAiIiIiIiLOUgZGRERERMQLWS/NwGgAIyIiIiLijbx0AKMpZCIiIiIikmMoAyMiIiIi4o2SdRllERERERERRykDIyIiIiLijbQGRkRERERExFnKwIiIiIiIeCNlYERERERERJylDIyIiIiIiBeyVhkYERERERERRykDIyIiIiLijbQGRkRERERExFnKwIiIiIiIeCNlYERERERERJylDIxkio1H9jodgqMaFKzodAiOalunr9MhOG7mho+cDsFRt9bs6XQIjnsq9zGnQ3DU7PzFnA7BcdNO7HI6BEdVzV3c6RDkHFYZGBEREREREWcpAyMiIiIi4o2UgREREREREXGWMjAiIiIiIt4o2ekAsoYyMCIiIiIikmMoAyMiIiIi4oV0FTIRERERERGHKQMjIiIiIuKNvDQDowGMiIiIiIg30iJ+ERERERERZykDIyIiIiLihbSIX0RERERExGHKwIiIiIiIeCOtgREREREREXGWMjAiIiIiIl5Ia2BEREREREQcpgyMiIiIiIg30hoYERERERERZykDIyIiIiLihawyMCIiIiIiIs5SBkZERERExBspAyOSfW6/oylr1s9lXch8Bgzsk6Hc39+fzyePZV3IfIIW/kzp60sCUPr6kkTE/MmSFdNYsmIao8eOSG3z02+TWLpyOiuCZzN67Ahcrpz38q/XvC6fLfqUL5Z+zoN9H8hQft9j9/LJ/Il8PPcj3vzuDYqWLOpAlJmvXvO6fL7oM75YOomH+j6YobzGzTfy4azxzN47k1vvapKurNfgnnwybwKfzJtAs3ZNsyvkbDN01Giatu1Ehy5POB1KlrmleQN+WDqFn5Z/Q9f+j2Qor31zTSbP+YRl++dzW9tm6cqKlSzK2O/e4fvFk/lu0ZcEliqeXWFnqrxN6lJu9qeUm/M5hR7LeOznv/cOyq/4njK/jafMb+Mp0LF1almlLTNSt5f8aFh2hp1lmrZoRNCqX1mwZip9nu6eobx+w5uYuuAbdkStoU2727M/wCzQ5LaGzF7xM3NW/8pjT3XLUF7vljr8Mu8r/oxYSeu7W6Rur3JjJb6f9TnTl/zA1EXfcuc9LbMz7ExTp9lNjF/4MR8tmch9fTtmKG/f+x7Gzf+QMXPG8ep3r1GkZBEAylYrx5u/vcPYeSlljds1ydBWch5lYC7AGPMS8AjgJmX82gf4AahnrY09p+4Ka22ji+zrN6AckA8oAuz1FPUFvr3APtsD1ay1b15gn2WBGdbaG/9x565yLpeLd0YP59723YgIj2LBkl+ZPWs+O7bvSq3TtdsDHD1ylLq1bue+jm0ZPvIFenV7BoCwvftp2qh9hv32fPRp/vrrOACTvxlPh/vu5NefZ2ZPpzKBy+Wi32v9GPzIEGIjY/lgxlhWBa1mf+j+1Dq7/9zNU22f5vSp09zdtS29X+rJqL7nfQnlGC6Xi/6v9eN/qf0ex8qgVen6HR0ew7sD36Njn/vTtW3QogEVb6zAE6374u/vx7s/v0PwwrWcOH4iu7uRZTrc1ZJH7m/PkJHvOh1KlnC5XDw36hme7vQc0ZExfDFrAkvnLCcsdF9qnUPh0Ywc8CaPPPFQhvbDxg7hy3FfsWbJOvLkzUNyTpwQ7nJR7JV+HOw5hMRDsZT5aSzHF6wmYff+dNX+mr2Y6JEfZ2huTyWw797+2RVtlnO5XAx/60W6dexLVMQhfgv6mvl/LGbXzr2pdSIORvJC/+E81q+rg5FmHpfLxStvvUDPB/pzKOIQP82dzII5S9idps+R4VEMfvpVevbtkq7tqROneLHfcPbtPUDRYgH8PO8rli1cyV/Hjmd3N/41l8vF4689wfDOLxMXGcfb00ezJmg1B0MPpNbZs2UPz7UdSMKp07TuciePDunBe/3eJuHk/9m77/goijeO45+5SwIiRUpIoUsVBAKC1B9NqnQbFlDsDWzYUFQExI6iKBZUULGAikgnIkWl994JkOSSEJoiLbnM748cIUcCRE3uyPl987oX2d2ZvWfubm9v9pndPcHIR0fginFRPKwEb0x7i1XzV3H0j7/82CLfyY9feTmR/w5B+4AxpgnQBahvra0DtAX2nq38uTovnuU9rbVRwF3Ar9baKM9j4Tnq/HS2zkugu6JBXXbu3M3umL2kpKTww3fTuLpzW68ynTq35evxkwCYPGkmLVs1Oe96T3VegoKCCAkOwdr8dXOn6lHViI+JJ2FPAqkpqcz7aT5N2jf2KrNm0VpOHD8BwKaVmykVXsofoeaq6lHViY9xZZvIv4QAACAASURBVLR7/k/zadre+/1OjE1k1+ZdWd7TClXLs3bJOtLcaRw/doKdG3fRoNUVvgw/zzWIqk2xokX8HUaeqVmvBrExccTvcZGakkr05F9o0aGZVxlXbALbN+3McsO2ilUr4AxysnTBCgCOHT3GiWMnfBZ7bilYpxope+JJiU2AlFT+nD6fwlc1Pn/FAFW3/uXs3hXL3t1xpKSkMnXSLNp2auVVJm6viy0bt5GWFhi/3urUr8WeXXuJ9bR5+qRoruronW2M2+ti68btWbaDmJ172L0r/SdMUmIyB5IPUKJkcZ/FnhuqRlXFFeMicU8iqSmp/DZlAVe2b+RVZv2idZz07P+2rtpCyYiSAMTviscV4wLgYOIBDicfpliJor5tgOQ6dWCyFwEkW2tPAFhrk6218acWGmMuMsbMNMbc7Zk+4vm/lTFmnjHmO2PMZmPMeGOMycHz9TfGrDTGrDPG1PCsq68xZpTn7zBjzCRjzBrPw6vDZIy51BizyhjT0FPvB09824wxr2Uq194Ys8jzXBONMYU9818xxmw0xqw1xrzhmXe9MWa95/kW/JsX8++KiAwjLtaVMR0fl0BEZJhXmchMZdxuN38cPpLxhVy+Qlnm//4TU2d+RZOmDbzqfffjZ2zbtYQjR44wedLMPG5J7ioZXop98fsyppNdyZQKL3nW8h1vbM+yect9EVqeKhVe0qvd+1zJlDxHuzPbuWknDVs1oEDBAhQtXpS6TeoQGhmaV6FKHggNDyUp0/uf5NpHaETO3sPylcvx5+EjvDJmCONmf0y/5+7Ll0NHg8JKkeI6/RqkJiQTFJZ1GyjSrjkVJ79P5MhnCcp08MIUCKHCdyMp/81bFL7q/Ad7LnRhEaG44hMyphPikwiLCIzhsmcTFh6KKy4xYzrBlUhYDreDzGrXq0lwcDB7YmJzM7w8VyK8JMnxpweq7Hftp2Q228ApbXu1Y+XcFVnmV61bleDgIBJ2J2RTK0Cl+eDhBxpClr3ZwPPGmK3Az8C31tr5nmWFgW+Az621n2dTtx5QC4gHfgeaAb+d5/mSrbX1jTEPAI+TnqnJ7B1gvrW2pzHG6YmhOIAxpronntuttauNMbWAKE8cJ4Atxph3gWPAIKCttfYvY8xTwGOeTlJPoIa11hpjLvE85/NAB2ttXKZ5PpFdny9LtuQsZRIT9lH7shYcPHCIulG1GP/NBzRp2Ckj+3Jdj9spUCCEjz4dQYuWTZg39/c8aUNeyK4rfLYkUpueralapxpPXP9k3gblCzn5PJzFigUrqVa3Gm//OILD+w+zaeUm3Knu3I5Q8lC2h4By+P47nU6iGtXm1vZ3kxiXxLAPnqdzr45M+Xp67gbpD2e8BEfmLuHPqfOxKSkU63U14a8MILbvQAB2tLkVd9IBgsuGU27cK5zYGkPKXlc2K80fsj0umM8y6n/bv/gePCW0dElee28IT/cfnO9GIOTod4FHy56tqFynCoNuGOg1v3jp4jz89mO889jb+a79klX+OxTlA9baI8AVwD3APuBbY0xfz+LJwGdn6bwALLXWxlpr04DVQMUcPOUPnv9XnKV8G2C0Jza3tfawZ36oJ57e1trVmcrPsdYettYeBzYCFYDGQE3gd2PMauA2z/w/gOPAGGPMNcCpkwN+B8Z6skzO7II2xtxjjFlujFl+IuWPHDQzZ+LjEihTNiJjOrJMOAmupLOWcTqdFC1WmIMHDnHy5EkOHjgEwJrVG9i1aw+Vq1T0qnvixElmTJvD1V28h6Vd6JJdyV7Zg1IRpdifuD9LuXrNo7ip/428cMdgUk6m+DLEPHFmu0MjSnEg8UCO63/97jfc3/FBnr7lGTCGuF3x568kF4wk1z5KZ3r/S0eEsi8h+Rw1vOtuXb+d+D0u3G4382f+RvXaVfMq1DyTmphMcKaj7UHhpUhN8t720w79iU1J394PT5xJwVqn2+lOSt9eUmITOLp0LQVqVvZB1HknIT6JiMjTF2MIjyxNYsK+c9TI/xJdSUSUOT0SITwijKQcbgcAFxe+mA++epu3Xx7NmhXr8yLEPLXflUypyNNZxZIRJTmQlHU/UKd5Xa7rdwMv3zmM1JOpGfMvKnwRz372Al+98SVbV23xScwXCpuW9w9/UAfmLDwdhXnW2heAfsCps4N/BzqdY2hY5gHWbnKW5TpVJ6flTzlM+rk5zc6Yn10MBojOdP5NTWvtndbaVOBK4HugBzATwFp7H+kZm3LAamNMllyttfYja20Da22DAsG5N5505Yq1VK5cgfIVyhIcHMw113VmxvQ5XmVmTp/DTbf0BKB7z44smL8YgJKlSmQMEalQsRyXVq5ATMxeLr64EGFh6T8AnE4n7Tq0YtvWnbkWsy9sWbOVMhUjCSsXRlBwEK26tWRx9GKvMpVrVeahVx7ihTte5PD+w2dZU/6yZc0WylSMJNzT7pbdWrLojHafjcPhoMgl6eeHVKpRiUsvq8SKBVmHFciFa9PqLZSrVJaIcuEEBQfRrnsbfp191tMHz6i7mSLFCnNJiWIANGhen11bd5+n1oXn+LqtBFeIJLhMGAQHUeTqlhz5xXsbcIaePqehcJvGnNyRfs6Do2hhTHBweplLinJRvZqc3O598n9+s3bVBipeWo6y5SMJDg6iS88OzJk5//wV87F1qzZS4dLylPG0+eqe7fhlVs5GdwcHBzFq7OtMnjCdWVPmnL/CBWjbmm1EVIqktGc/0LxrC5ZFL/UqU6nWpdz/8oMMv3Oo1/4vKDiIpz9+lnk//MLCafln1IWcm4aQZcMzLCvNWrvNMysK2A3UJn1o1XPA+8D9Pgppjue53vYMIbvYM/8k6Z2OWcaYI9bar86xjsXAe8aYKtba7caYQkBZ0oe6FbLWTjfGLAa2AxhjKltrlwBLjDFdSe/IZD3cnwfcbjdPDniR73/8DKfTyfgvJrJ50zYGDnqY1SvXM2P6HL4YN4EPxrzJijVzOHjwEHf2fQSAps0aMnDQI7hTU3G70xjw8PMcOniY0NIl+WrChxQoEILD6eTX+Yv4dMy5Xq4LT5o7jfeeG83wL4fhcDqZ/e1sdm/dw60D+rB17VYWRy/h7mfv5KJCBRn0wTMAJMXvY/AdL/o58n8nzZ3GqOfeZ/iXL+FwOpj17Wx2b93tafc2Fkcvplrdarzw8XMUKVaExm0b0eexPtzT9l6cwU5GfJ9+da6jR47yykOvkeYOjJN6T3nihVdYtmothw79wVU9evPAnX24tmuH81fMJ9xuN288O5KRX72Ow+lg6jcz2LU1hrufuJ3Na7bw6+yFXFa3Oq9+MowilxSmebsm3P14X25ufTtpaWm8O3Q0oyaMAGPYsnYrk8dP9XeT/j53GklDR1P2k2HgcHL4+9mc3L6Hkv37cHz9Vv6au4TifbpTuHVjrNtN2uE/SRj4JgAhlcsR/mJ/bJrFOAwHPp6Q5epl+Y3b7ebFp19l7MT3cDgcfPfVT2zbspNHnr6Pdas3MmfmAmrXq8nocW9SrFhR2nRowcNP3Uen5lkvP51fuN1uhj79Gp98+w4Op5Pvv/qJ7Vt20v+pe1m/ehNzZy3g8qiajBr7GkWLFaV1++b0e/JeurboRcfu7WjQpB6XlChGzxu7ADDwoRfZvH6rn1uVc2nuND5+7gNe+OJFHE4Hc779mb1b93DTY7ewfd02lkUv5bZnb6dgoYI8MfppAPbF7+PlO4fRrEtzal5ZiyKXFKHNdemX1H5nwNvEbNx1rqcMGIF6FTKjcYBZGWOuAN4FLgFSSf9Rfw+wHGhA+g/5T4F91tonPZ2HwsaYVsDj1tounvWMApZba8d6pr2We+bF4LmMsjGmAfCGtbaVZ8haA2ttP2NMGPARcCnpGZX7AReeyyh7zlGJBoaRfm5MA2ttP8/6p3rWOc8Y0wZ4FSjgefpBwDLSh6EVJD1L84a1dpwx5gegqmfeHOARe44PS/HCVf7TH6Qri+e/YSm5yZ45IP8/aNqq9/0dgl/9r84d/g7B78YVvcjfIfjV1cmJ5y8U4IId2Y64/s+4rGD+vM9Sbpq0Z0pOLt7kM4mtW+b5Djps7nyft1kdGMkV6sCoA/Nfpw6MOjDqwKgDow6MOjDqwPiGzoEREREREQlE1uT9IweMMR2NMVuMMduNMU+fpcwNntt6bDDGnHOcv86BERERERGRPOE5f/s9oB0QCywzxvxkrd2YqUxVYCDQzFp70Bhzzps7qQMjIiIiIhKALpCT+K8EtltrdwIYY74BupN+q49T7gbes9YeBLDWJmVZSyYaQiYiIiIiIv9I5vsCeh73nFGkDOm3/Tgl1jMvs2pANWPM78aYxcaYjud6TmVgREREREQCkE3L+/PrrbUfkX613LPJLogzLy4QRPrVb1uRfpuPX40xl1trD2W3QmVgREREREQkr8SSfj/BU07dh/DMMpOttSnW2l3AFtI7NNlSB0ZEREREJADZtLx/5MAyoKoxppIxJgS4EfjpjDI/Aq0BjDGlSB9StvNsK1QHRkRERERE8oS1NhXoB8wCNgETrLUbjDFDjDHdPMVmAfuNMRuBucAT1tr9Z1unzoEREREREQlANof3aclr1trpwPQz5j2f6W8LPOZ5nJcyMCIiIiIikm8oAyMiIiIiEoAukPvA5DplYEREREREJN9QBkZEREREJAD54j4w/qAMjIiIiIiI5BvKwIiIiIiIBCB75v3uA4QyMCIiIiIikm8oAyMiIiIiEoB0DoyIiIiIiIifKQMjIiIiIhKAAjUDow6MiIiIiEgA0kn8IiIiIiIifqYMjIiIiIhIANIQMhE5q7tTS/g7BL+6+88l/g7B7/5X5w5/h+BXv6791N8h+F3HqPv8HYJfFXQG+zsEvzvuTvF3CH6VQpq/Q5D/CHVgREREREQCkLWBmYHROTAiIiIiIpJvKAMjIiIiIhKAbICO6lMGRkRERERE8g1lYEREREREAlCazoERERERERHxL2VgREREREQCkK5CJiIiIiIi4mfKwIiIiIiIBCCbpgyMiIiIiIiIXykDIyIiIiISgKz1dwR5QxkYERERERHJN5SBEREREREJQDoHRkRERERExM+UgRERERERCUBpug+MiIiIiIiIfykDIyIiIiISgGyAZmDUgRERERERCUC6jLKIiIiIiIifKQMjIiIiIhKAdBK/iIiIiIiInykDIyIiIiISgAL1JH5lYOSCdFXbFixdOZsVa+bwyGP3ZlkeEhLCJ+NGsmLNHKLnfke58mUAKFe+DPH71rNg4U8sWPgTI0YOyagzcdKn/LpoCguXzWDEyCE4HPnj4x/Wug4dfn2djgvfpHq/rmctV6bzlVznGk/xupUAKB51KW2jh6c/fh5OZKcGvgr5X8uL9z84OJi33h3GslXRLFk5i67dO/isPf9W41ZX8u2vnzPx9/H06XdzluVRjeowbtZH/LZnDq07t/RaFlamNCO/fp1v5o/j63ljiSgb7quwfWbQ8BG06HwjPXrf5+9Q8kzDVg0YO/8TPv/tM258sFeW5bUb1eaDGe8xO2YGLTr/z2vZPc/exSdzPuLTuWN4cMgDvgo5VzVv3Zipv09gxuLvuKv/rVmWX9E4ionR41gT9zvtu7TJmF+jVlXGTxvD5Plf88PcL+nYva0vw85VLdo0JXrxD/yydDL3PtQ3y/KGTeoz+ZfxbElYSseuV3kt++zbUazaMZ+Pvxrpo2hzX/2WV/DB3A/5aMHHXPfA9VmW97irB+/PGc27s0bx0tcvEVomNGPZi58P4Zt13/L8Zy/4MmTJQ8rA5CPGmCPW2sK5uL6KwFRr7eXGmAbArdbah3Jr/f+Uw+Hg9RGD6dntNuLjEvhlwQ/MmD6HLZu3Z5Tpc9v1HD50mCvqXsU113Vm8NAnufO2hwGI2bWHFk27ZVnvHbc+xJ9/HgFg3PhR9LimEz98N803jfqnHIZ6w/vya6+XOeo6wFUzhhI/eyV/bo3zKhZ0cUGq3NWB/StOv0Z/bIllTsdBWHcaBUtfQts5w3HNXol1p/m6FX9LXr3/A558gOR9+2lYrx3GGIqXuMRnbfo3HA4Hjw9/mIdufJwk1z4+m/4Bv876nZhtuzPKJMYlMfSRV7j5vqw/bF8Y+Qxj3/mCpQtWcFGhi0izF/b7/0/0uLodN1/bjWeGvuHvUPKEw+HgoWH9ePLmp9nnSub9ae+yaPYidm/bk1EmKS6J1x57g+vvvc6rbs0ralKrQS3ubpfeuRs5aQR1m9RhzaK1Pm3Dv+FwOHj2lSe4+4b+JMYn8e2sscyd9Ss7tu7KKOOKS+TZh4fS9/5bvOoeO3acgf1eZM+uvYSGlWJi9Dh+n7uYP/844utm/CsOh4PBrz7Fbdc9QEJ8IpOiv2TOzPlsz/QaxMe6eLLfYO5+sE+W+h+P+pyChQpy023X+jLsXONwOLh/2P0MumUQ+13JvDXlLZZEL2bvtr0ZZXZs2MmjnR/hxPETdOp9Nbc/cwevPfgqAD98+D0FLipAx1s6+asJfqOrkElAs9YuvxA6LwBXNKjLzp272R2zl5SUFH74bhpXd/Y+atapc1u+Hj8JgMmTZtKyVZPzrvdU5yUoKIiQ4BBsPtiqS9SrzJGYRP7asw+b4mbv5MVEdrgiS7laT13H1vemknbiZMY897GTGZ0VR4FguPCbC+Td+9+7z3W89cYHAFhrObD/YO4Hnwdq1qtBbEwc8XtcpKakEj35F1p0aOZVxhWbwPZNO7Fp3m9yxaoVcAY5WbpgBQDHjh7jxLETPovdVxpE1aZY0SL+DiPP1IiqTlxMPK49CaSmpDJ38nyatm/qVSYxNpGdm3Zl+QxgLSEFQggKCSI4JBhnUBAH9+WPz/4ptevXZO+uWGJ3x5OSksr0H6Np3bGFV5n4vS62btyOTfPuoO/euZc9u9J/5O5LTOZA8kGKlyzus9hzS936l7N7Vyx7d8eRkpLK1EmzaNuplVeZuL0utmzcRlpa1oMUC39dyl9H/vJRtLmvWlQ1XDHxJHq2gQVTFtC4fWOvMusWreXE8fTvty2rNlMqolTGsjW/r+HYkWM+jVnyljow+ZAxppUxZp4x5jtjzGZjzHhjjPEse8UYs9EYs9YY84Zn3lhjzHWZ6mc59ORZ51TP34ONMZ96nmOnMcanHZuIyDDiYl0Z0/FxCUREhnmVicxUxu1288fhI5Tw7JTKVyjL/N9/YurMr2jS1HvY1Hc/fsa2XUs4cuQIkyfNzOOW/HsXhZfgWNz+jOljrgNcFO69873k8gpcFFkS18+rstQvUa8y7ea9Svu5r7DyqU8v+OwL5M37X7RY+o/bZ557lHm/TeazL94ltHRJXzTnXwsNDyUpfl/GdJJrH6ERoeeocVr5yuX48/ARXhkzhHGzP6bfc/flm6GTclqpiFLsc53+DOxL2EepiJx9fjeu3MTqhauZuOIbJqz8huXzl7Nn+97zV7yAhIWXxhWfmDGdGJ9EWHjOtoHMaterSVBwEHtjYnMzPJ8IiwjFFZ+QMZ0Qn0RYRGk/RuRbJcNLsi8+OWM62ZVMybCzbwPte7VnxdzlvgjtgpdmTZ4//EF7svyrHvAIUBO4FGhmjCkB9ARqWWvrAMP+xfprAB2AK4EXjDHB/zLeHPP0xbxkyZacpUxiwj5qX9aCls268ezTL/Hxp29RpMjpUXfX9bidGlWaEFIghBYtz3/U3u+y+17I/FoYQ90Xe7N28Phsqx9YtYPoVk8xp9Nz1OjfLT0Tc4HLi/c/KCiIMmUjWLJ4Ba2ad2fZklUMfWlgXjUhV2XT1ByPCXA6nUQ1qs07Q0ZzR6f7KFM+gs69OuZugOIXOc0gR1aMpHzV8vRqeDO9GtxEvWZR1G5UO4+jy2XZbAP2b6aUS5UuycujBjPokWH5Ivt+puy+FwN2bFB2sv3Oz75oq56tqVKnKt9/+H0eByX+pA5M/rXUWhtrrU0DVgMVgT+A48AYY8w1wNF/sf5p1toT1tpkIAkIO7OAMeYeY8xyY8zyEyl//Iun8hYfl0CZshEZ05FlwklwJZ21jNPppGixwhw8cIiTJ09y8MAhANas3sCuXXuoXKWiV90TJ04yY9ocru5y4Z/Mecx1gIvKnD7KdFFECY4lHsqYDipckKI1ytHyh0F0Wvo2JepXoenYARkn8p/y57Z4Uo+eoFiNsj6L/Z/Ki/f/wP6D/PXXUab+NBuAyZNmUCeqlo9a9O8kufZROvL00ebSEaHsS0g+Rw3vulvXbyd+jwu32838mb9RvXbVvApV8kiyK9kr6xYaHsr+hAM5qtu8YzM2rdzM8aPHOX70OEvnLqNm/Rp5FWqeSHQleWVhwyJLk5TDbQDg4sIXM3r8CN555QPWrlifFyHmuYT4JCIiT1+AIzyyNIkJ+85RI7DsdyUTGnl6SFipiFIcSNqfpVzd5lH06teLoXcOIfVkqi9DvGBZa/L84Q/qwORfmQeyu4Ega20q6RmT74EewKkxUql43mvPULOQf7L+MwtYaz+y1jaw1jYoEFz077fgLFauWEvlyhUoX6EswcHBXHNdZ2ZMn+NVZub0Odx0S08AuvfsyIL5iwEoWapExhCZChXLcWnlCsTE7OXiiwsRFpb+A8DpdNKuQyu2bd2ZazHnlYOrd1K4UjiFyoVigp2U694Y16wVGctT/zzGlFr3MePKR5hx5SMcWLmdhX3f5OCaXel1nOmvRaGypShSOYK/9l74O7y8eP8BZs34heYtGgHQolUTr4sCXMg2rd5CuUpliSgXTlBwEO26t+HX2QtzWHczRYoV5pISxQBo0Lw+u7buPk8tudBsXrOFMpXKEO75DLTu3pKF0YtyVDcpLok6jWvjcDpwBjmp07gOe7blryFk61dtovyl5ShTPoLg4CCu7tGOubMW5KhucHAQ74x9lZ8mzmD2lF/yONK8s3bVBipeWo6y5SMJDg6iS88OzJk5399h+czWNVuJrFSGsHJhBAUH0aJrC5ZEL/Eqc2mtS+n3cj+G3jmEw/sP+ylS8RVdhSyAGGMKA4WstdONMYuBU7/QYoArgAlAd+CCHkfkdrt5csCLfP/jZzidTsZ/MZHNm7YxcNDDrF65nhnT5/DFuAl8MOZNVqyZw8GDh7iz7yMANG3WkIGDHsGdmorbncaAh5/n0MHDhJYuyVcTPqRAgRAcTie/zl/Ep2O+8nNLz8+601j9zFj+9/VTGKeDmG/m88fWOGo+cS0H1+zCNXvlWeuWalSd6v26YlPcWJvGqoGfcfLAhX/lnbx4/wEGP/caH4x5g5dfHURy8gH63feUP5uZY263mzeeHcnIr17H4XQw9ZsZ7Noaw91P3M7mNVv4dfZCLqtbnVc/GUaRSwrTvF0T7n68Lze3vp20tDTeHTqaURNGgDFsWbuVyeOn+rtJue6JF15h2aq1HDr0B1f16M0Dd/bh2q755zLZ55PmTuPd50bx6vjhOBwOZnw7i91bd9P38VvZsmYri6IXU71uNV4c8wKFixWhSbvG3PZYH+686h4WTPuVes2iGPPzR2Aty+YtZ9HPi/3dpL/F7Xbz0sA3+Oibd3A4HUz6ego7tuyi35P3sGHNJubO+pXLoy5j5GevUfSSIrRq/z8efOJuure8iQ7d2nJF43pcUrwYPXp1BuDZh4awecM2P7fq73G73bz49KuMnfgeDoeD7776iW1bdvLI0/exbvVG5sxcQO16NRk97k2KFStKmw4tePip++jUPP1yw99M+YRLq1bk4osv4re1Mxj48BB+nZuzTvCFIM2dxgfPjWbIF0NxOB1EfxvNnq17uOWx3mxbt42l0Uu449k7KVioIE+PTh8evC9+H0PvTL+U/qvfvUrZyuUoeHFBxi4ZxztPjGTlgrPvPwOJv85RyWsmP44F/a86dRllY0wr4HFrbRfP/FHAcmAWMBkoSPqo4TesteOMMWGe+Q5gDtDfs56KnL6McsY6jTGDgSPW2lMXAVgPdLHWxpwttuKFq/ynP0gfF2nk7xD86u4/l5y/UICrXvTCH56Xl35d+6m/Q/C7jlGBex+anEhI0VHv4+4Uf4fgV5cVijh/oQA3dc+0C6rHsCTymjz/fdYo/geft1kZmHzk1D1grLXzgHmZ5vfLVOzKbOolApmvNzjQMz8GuPzMdVprB59R//J/G7uIiIiI+FagHl3WOTAiIiIiIpJvKAMjIiIiIhKAAvUcGGVgREREREQk31AGRkREREQkAPnrPi15TRkYERERERHJN5SBEREREREJQGn+DiCPKAMjIiIiIiL5hjIwIiIiIiIByBKY58CoAyMiIiIiEoDSAvROlhpCJiIiIiIi+YYyMCIiIiIiASgtQIeQKQMjIiIiIiL5hjIwIiIiIiIBKFBP4lcGRkRERERE8g1lYEREREREApBuZCkiIiIiIuJnysCIiIiIiAQgnQMjIiIiIiLiZ8rAiIiIiIgEIJ0DIyIiIiIi4mfKwIiIiIiIBKBAzcCoAyO54mRaqr9D8KuJIX/5OwS/uqXUFf4Owe/6F/zD3yH4Vceo+/wdgt/NXP2Bv0Pwq6Z1+vo7BL/bcjDW3yH4Vc/C1f0dgvxHqAMjIiIiIhKAdBUyERERERERP1MGRkREREQkAKUFZgJGGRgREREREck/lIEREREREQlAaToHRkRERERExL+UgRERERERCUDW3wHkEXVgREREREQCUKDeyFJDyEREREREJN9QBkZEREREJAClGZ3ELyIiIiIi4lfKwIiIiIiIBKBAPYlfGRgREREREck3lIEREREREQlAugqZiIiIiIiInykDIyIiIiISgNIC8yJkysCIiIiIiEj+oQyMiIiIiEgASiMwUzDKwIiIiIiISL6hDIyIiIiISADSfWBERERERET8TBkYEREREZEApKuQifhQu3YtWbV6DmvXzWPAgPuzLA8JCWHc56NYu24e8+b/SPnyZb2Wly0bSWLSBh5++O6MeaM/eI2YmOUsMtHnJAAAIABJREFUWzYrz+PPTXVb1uOtX95j5PzRdL//mizLO9/VjTd/fpfXZr7NoK+GUKpMqNfyiwpfxOgln3D7kLuz1M0vLmtZl+fmvMUL80bS7v7uWZa3ubMzz0a/ycAZr9F//CCKlymVsaz707fw7Ow3GPTzCK57oa8Po849hZpfQaUZH1Np1ieUuPv6LMuL9mxL5YXfUGHSKCpMGkWx6zpkLKu2YWrG/DLvv+DLsHNVw1YNGDv/Ez7/7TNufLBXluW1G9XmgxnvMTtmBi06/89r2T3P3sUncz7i07ljeHDIA74K2WcGDR9Bi8430qP3ff4OJc80aXUl3/36JT/8/hW39bsly/J6jeryxawxLNrzC206t/RatnjvXMZHf8L46E94c+zLvgo5V3Ro34oN6xeweeNvPPnEg1mWh4SE8NX40Wze+BsLf5tChQrp+8ISJYrz8+yJHDqwlZFvD8t23ZN++IzVq+bkafx5qXrLujw5502envcWre/vlmV5k1vaMmDmqzw6/WUenPgCYVXK+CFKOcUY09EYs8UYs90Y8/Q5yl1njLHGmAbnWp86MP8Bxhi3MWa1MWaNMWalMaapZ35Fz4dkaKaypYwxKcaYUZ7pwcaYx30Zr8PhYMRbQ+jZoy9X1G/H9dd3o0aNKl5lbut7A4cOHaZO7VaMevcThg7z3hZefe05Zs+e5zXvyy++o0eP2/I6/FxlHA7uGHovL982hMfa9qdZt/9Rpqp3Zy1mw04GdhnAkx0fYcn0hdwy0LuNNwy4mY1LNvgy7FxlHIYbhtzB+31fZli7x7iiWzPCz9gR7d0Yw2tdB/JypydZNWMJPQam/8CpVL8alzaozvCOT/BS+wFUqFuZqo1r+qMZ/5zDQdjzDxJ793Ps6nIvRTq3IqRy+SzF/pwxn909+7G7Zz8Of3e6k26Pn8yYH/fAi76MPNc4HA4eGtaPgX2e5Y7Wd9OmeysqVPV+DZLiknjtsTeY8+MvXvNrXlGTWg1qcXe7+7jrqnuoUbcadZvU8WX4ea7H1e34YET2P1IDgcPh4Mnhj/LwLU9wQ6tbad/9KipVreBVJiEukRcfGc6sST9nqX/i+AluaXcnt7S7kwF9B/oq7H/N4XDwzsiX6NK1N7XrtqZXrx5cdllVrzJ33H4TBw8epkbN5rz9zse8PPxZAI4fP84Lg1/jyaeGZrdqevToxJEjf+V5G/KKcRh6DrmdMX1f5fV2j1OvW9MsHZSVk3/nzY5P8dbVA5n74VS6PtfHT9H6V5oPHudjjHEC7wGdgJrATcaYLDtjY0wR4CFgyfnWqQ7Mf8Mxa22UtbYuMBDIfAhqJ9Al0/T1gF9/7TZoEMXOHbuJidlLSkoK3303hS5d2nuV6dK5PeO//B6ASZOm06pV09PLurYnZtceNm3a5lXn99+XcuDA4bxvQC6qElWVxBgXSXsTcaeksnDKbzRs18irzIZF6zl5/CQA21ZtoWREyYxllS6vzCWlLmHtgtU+jTs3VYyqQvLuRPbvTcKd4mbllIXUad/Qq8y2RRtI8bwGMau2cUn4qdfAElwgmKDgIIJCgnEGOfljX/76DBSsU42UPfGkxCZASip/Tp9P4asa+zssn6oRVZ24mHhcexJITUll7uT5NG3f1KtMYmwiOzftwqadccqqtYQUCCEoJIjgkGCcQUEc3HfQh9HnvQZRtSlWtIi/w8gztepdxt6YOOL2uEhNSSV68hxadmjuVcYVm8D2TTuzvv/52JUN67FjRwy7du0hJSWFCRMm061rB68y3bq254svJgLw/ffTaNM6/XU5evQYvy9cxvHjJ7Ks9+KLC/How/cw/OWRed+IPFI+qgr7dydwwLNfWD1lEbXaex+wP3HkWMbfIYUKgA2cz0Y+dCWw3Vq701p7EvgGyDqcAoYCrwHHz7dCdWD+e4oCmffex4BNmVJ1vYAJPo8qk8jIMGLj4jOm4+JcRESGnbWM2+3mjz/+pGTJ4hQqdBGPPXYfw4fn3y/mzEqEl2C/Kzljer9rP8XDS5y1fOtebVk9byUAxhj6DLqdL4ePy/M481KxsBIcjN+fMX3QtZ9iYcXPWr7JDa3ZOC+9w7Zr5Ta2LdrAS8s+ZPjSD9m0YA2JO+LyPObcFBRWihTXvozp1IRkgsJKZilXpF1zKk5+n8iRzxIUfnoInSkQQoXvRlL+m7cofFUTn8Sc20pFlGJfptdgX8I+SkVkfQ2ys3HlJlYvXM3EFd8wYeU3LJ+/nD3b9+ZVqJIHQsNLkRiflDGd6NpHaEToOWp4CykQwrgZH/HplNG07Nj8/BUuEJFlwtkbe3pfGBvnIjIy/Kxl3G43hw//QcmSZ/9+BBgy+ElGvP0hR48eO2e5C1mxsOIcyrRfOHSW/ULTPu14ev7bdHn6Zn4cnL/3hf+U9cEjB8oAmb94Yz3zMhhj6gHlrLVTc7JCncT/33CRMWY1UBCIANqcsfwb4EZjTALgBuKBSN+GeJoxWc84s2ceOTlLmUGDHmXUu5/w119H8yo8nzLZ3YDqLN8WzXu2pHLtKgzulT6EoP2tnVg9d4VXByg/yu7zcLbXoGGP5pSvU5mRvQYDUKpCGGFVyjCocfp5VP2/HETlK9eyY+mmPIrWR85o/5G5S/hz6nxsSgrFel1N+CsDiPUMldnR5lbcSQcILhtOuXGvcGJrDCl7XX4IOndl+U44i8iKkZSvWp5eDW8G4PWvX6F2o9qsW7IuL8OTXJSjfcI5dG14PcmJ+ylTPoL3J77N9k07idsdf/6KfpaTdmdf5uzrrFu3FpWrVGTAE4MzzpfJl3LY7oVfRLPwi2jqdWtK2/49+WbAaB8E999jjLkHuCfTrI+stR9lLpJNtYx3zBjjAN4C+ub0OdWB+W84Zq2NAjDGNAE+N8Zcnmn5TNLTdonAtzldaeYPbEhwCYKCcmcIQ1xcAmXLnO4/lSkTQYIryatMvKdMfFwCTqeTokWLcODAIRo0jKJHz6sZ9tJAihUrSlpaGsdPnODDDz7Pldh8bX/CfkpGnD6aXjKiJAcTD2QpV7tZHa7pdx2DbxhE6slUAKrVr06NhjVp16cTBS8uSFBwEMf/Os7Xr37hs/hzw6GE/RSPPH20vXhESQ4nZR0CVL1ZbTr0u4a3ew3OeA3qdriSmFXbOHk0fRjFhnmrqVSvar7qwKQmJhOc6WhzUHgpUpP2e5VJO/Rnxt+HJ84k9PE7MqbdSemfl5TYBI4uXUuBmpXzXQcm2ZXsdcQ9NDyU/QlZt4PsNO/YjE0rN3P8aPqIhKVzl1Gzfg11YPKRJNc+wiJLZ0yHRYSSnJDzAzPJienbS9weFysXrqb65VXzRQcmLtZFubKn94Vly0TgciVmWyYuzoXT6aRYsaIcOHD2IZKNG11B/Xq12b51MUFBQZQuXZI50RO5ql3Wi4NcyA4nHOCSTPuFSyJK8kc2+4VTVk9ZxDXD7vRFaBccX1yFzNNZ+egcRWKBcpmmy5J+sPyUIsDlwDxPpzwc+MkY081auzy7FWoI2X+MtXYRUAoIzTTvJLACGAB8/zfW9ZG1toG1tkFudV4AVqxYQ+UqFalQoSzBwcFcd11Xpk2L9iozbXo0t/S+FoCePa9m/vyFALRvdwM1L2tOzcua8957n/LG6+/l284LwI412wivFEFoudI4g4No2rU5y6OXepWpWKsSd738AK/dOZw/9p8+v+Pdh9/iwaZ307/5PXz50lgW/DA333VeAHav2UFoxXBKlg3FGeykftemrI32/j4rW6siNw6/iw/veo0j+//ImH8wPpkqjWricDpwBDmp2ugyErbH+roJ/8rxdVsJrhBJcJkwCA6iyNUtOfLLYq8yztDTQycKt2nMyR3pmXpH0cKY4OD0MpcU5aJ6NTm5fY/vgs8lm9dsoUylMoSXCycoOIjW3VuyMHpRjuomxSVRp3FtHE4HziAndRrXYc82DSHLTzau3kz5SmWJLBdBUHAQ7bpfxYLZv+eobpFihQkOSd8GipUoRp2Gtdm1NSYPo809y5avpkqVSlSsWI7g4GBuuKE7U6bO9iozZeps+vRJ73xce21n5s479+vy4UefU77iFVSp1piWrXuwddvOfNd5Adi7ZgelKoZTwrNfiOrahA3RK7zKlKp4erjdZW3qkRyT4OswLwgXwkn8wDKgqjGmkjEmBLgR+OnUQmvtYWttKWttRWttRWAxcNbOCygD859jjKkBOIH9QKFMi94E5ltr92c7ZMeH3G43Ax57nsk/fY7T6eTzzyewadM2Bj33KCtXrmP6tJ8ZN3YCYz4Zwdp18zh48BC33dr/vOsdO/Yd/teiMSVLFmfrtkUMG/YWn4/z6+k+55XmTuPT5z/mmc9fwOF0Mm/Cz8Ru28v1j93EzrXbWfHzMno/05eChQry6PtPApAcv4/X7xru58hzT5o7jQnPf8qDnz+DcTpYPGEeCdti6fzo9exZt5N1P6+gx8DeFChUkDvffxSAg3HJfHj366yavphqTS/nmVlvYK1l0/zVrJ+z0s8t+pvcaSQNHU3ZT4aBw8nh72dzcvseSvbvw/H1W/lr7hKK9+lO4daNsW43aYf/JGHgmwCEVC5H+Iv9sWkW4zAc+HgCJ3fkvw5MmjuNd58bxavjh+NwOJjx7Sx2b91N38dvZcuarSyKXkz1utV4ccwLFC5WhCbtGnPbY32486p7WDDtV+o1i2LMzx+BtSybt5xFPy8+/5PmI0+88ArLVq3l0KE/uKpHbx64sw/XnnGyd37mdrt57dm3eeerN3A6Hfz0zXR2bo3h3ifuYNOaLSyY/Ts169bgtU+GUfSSIjRv15R7H7+DXq1vo1LVigx89XHS0tJwOByMe288u7bt9neTcsTtdvPwI4OYPu0rnA4HY8d9y8aNWxn8wuMsX7GGqVOj+fSzbxg39h02b/yNgwcPcXPv05cJ3751MUWLFiYkJITu3TrSqfNNWS5uk1+ludOY9PxY7v58IMbpYNmEeSRui6XDo9exd90uNv68gma3tadqs9q4U1M5dvgvDR/zI2ttqjGmHzCL9N+gn1prNxhjhgDLrbU/nXsNWZm/M45U8idjjBs4NV7CAM9Ya6cZYyoCU621l59Rvi/QwFrbzxgzGDhirX3jXM9xcaGK/+kPUpfQKH+H4FehpoC/Q/C7/gX/OH+hAPbAEbe/Q/C7mas/8HcIftW0Tl9/h+B3q5J3+DsEv3oksoW/Q/C7N2K+vqBuHflh2d55/vvs3tgvfd5mZWD+A6y1zrPMjyF9zOGZ88cCYz1/D867yERERERE/h51YEREREREApC9oPJBuUcn8YuIiIiISL6hDIyIiIiISADK4VXC8h1lYEREREREJN9QBkZEREREJAApAyMiIiIiIuJnysCIiIiIiASgQL1JnzIwIiIiIiKSbygDIyIiIiISgNJ0HxgRERERERH/UgZGRERERCQA6SpkIiIiIiIifqYMjIiIiIhIAFIGRkRERERExM+UgRERERERCUC6D4yIiIiIiIifKQMjIiIiIhKAAvU+MOrAiIiIiIgEIJ3ELyIiIiIi4mfKwIiIiIiIBCCdxC8iIiIiIuJnysCIiIiIiASgtADNwagDI5ILNp9I8ncIfvX9gd3+DsHvZhQN83cIflXQGezvEPyuaZ2+/g7BrxauHevvEPyuSNlW/g7BrxalJPo7BPmPUAdGRERERCQA6SpkIiIiIiIifqYMjIiIiIhIAArMM2CUgRERERERkXxEGRgRERERkQCkc2BERERERET8TBkYEREREZEAlGb8HUHeUAZGRERERETyDWVgREREREQCUFqAXodMGRgREREREck3lIEREREREQlAgZl/UQZGRERERETyEWVgREREREQCkO4DIyIiIiIi4mfKwIiIiIiIBCBdhUxERERERMTPlIEREREREQlAgZl/UQdGRERERCQg6SR+ERERERERP1MGRkREREQkAOkkfhERERERET9TBkZEREREJAAFZv5FGRi5QLVr15JVq+ewdt08Bgy4P8vykJAQxn0+irXr5jFv/o+UL18WgCsa1GXR4uksWjydxYtn0LVbh4w6DzxwO8uWzWLZ8tk8+OAdPmvLv9W0dSMm//Y1UxZN4I5+fbIsr984im9mf8aK2AW07dI6y/KLCxcietVkBg5/zBfh5ooO7VuxYf0CNm/8jSefeDDL8pCQEL4aP5rNG39j4W9TqFAh/f0vUaI4P8+eyKEDWxn59jCvOnOiJ7Jh/QKWL5vN8mWzCQ0t6ZO25LYWbZoSvfgHflk6mXsf6ptlecMm9Zn8y3i2JCylY9erfB9gHmjeujFTf5/AjMXfcVf/W7Msv6JxFBOjx7Em7nfad2mTMb9GraqMnzaGyfO/5oe5X9Kxe1tfhp2rmrS6ku9+/ZIffv+K2/rdkmV5vUZ1+WLWGBbt+YU2nVt6LVu8dy7joz9hfPQnvDn2ZV+F7FODho+gRecb6dH7Pn+HkqvatWvJ2rVz2bBhAY8//kCW5SEhIXzxxXts2LCABQsmZ3wXNmhQlyVLZrBkyQyWLp1Jt0z7wg8/fJ09e1ayYkW0z9qRGxq1asjXC8bx7W9f0PvBm7Isr9uoDp/O/JD5u6Np1blFxvz6TaMYO/ujjMcvO2byvw7NfBm65IHzdmCMMW5jzGpjzAZjzBpjzGPGGIdnWQNjzDvnqd/XGDPq7wRljHnm75Q/o+5YY8wuT8wrjTFN/mb9I57/I40x3/3TOP7G8w02xsR54l1tjHkll9ffwxhTM9P0EGPMBb0XdzgcjHhrCD179OWK+u24/vpu1KhRxavMbX1v4NChw9Sp3YpR737C0GFPA7BxwxaaN+tKk8ZX06PHrbz7zks4nU5q1qzG7bffSIsW3WncqBOdOrWhcuWKfmjd3+NwOHjm5cd54OYB9GxxMx17tuXSahW9yiTEJfDcw8OYMSn7ndGDT93D8kWrfBBt7nA4HLwz8iW6dO1N7bqt6dWrB5ddVtWrzB2338TBg4epUbM5b7/zMS8PfxaA48eP88Lg13jyqaHZrvvWW/vRoGF7GjRsz759+/O8LbnN4XAw+NWnuKNXfzo0u5au13SkSrVKXmXiY1082W8wU76f6acoc5fD4eDZV57gvpsfodv/buTqnu2pfEabXXGJPPvwUKb9MNtr/rFjxxnY70W6t7yJe298hKeHPkqRooV9GX6ucDgcPDn8UR6+5QluaHUr7btfRaWqFbzKJMQl8uIjw5k16ecs9U8cP8Et7e7klnZ3MqDvQF+F7VM9rm7HByOGnb9gPuJwOBg5chjdu99GVNRV3HBDN2rU8P4u7Nu3F4cOHaZWrRa8++4Yhg1Lf383bNhC06ZdaNSoE9263cqoUS/jdDoB+OKLiXTrlvVAwIXM4XAw4KWHGdD7aW5pfTtte7Sh4hnbQGJcIi89+irRP87xmr9y4Wr6tr+Hvu3vof8NAzhx7DhL5y/3Zfh+leaDhz/kJANzzFobZa2tBbQDrgZeALDWLrfWPpQHcf3jDozHE9baKOBp4MN/sgJrbby19rq/U8cY4/wnzwW85XmNo6y1T//DdZxNDyCjA2Otfd5am3UPdwFp0CCKnTt2ExOzl5SUFL77bgpdurT3KtOlc3vGf/k9AJMmTadVq6ZA+g8Wt9sNQIECBbA2PXlavXoVli5blbH819+WeB2RulBdXq8me3fFErcnntSUVGb++DOtOvzPq0z83gS2bdpBWlrWr5HL6lSnZGgJFs1f6quQ/7UrG9Zjx44Ydu3aQ0pKChMmTKZbV+/3qlvX9nzxxUQAvv9+Gm1aNwfg6NFj/L5wGcePn/B53L5Qt/7l7N4Vy97dcaSkpDJ10izadmrlVSZur4stG7dl+3nIj2rXT98GYnfHk5KSyvQfo2ndsYVXmfi9LrZu3I49o827d+5lz669AOxLTOZA8kGKlyzus9hzS616l7E3Jo64PS5SU1KJnjyHlh2ae5VxxSawfdNObFqgDhg5twZRtSlWtIi/w8hVDRtGeX0XTpw4ha5dvfeFXbu258sv04+1/vDDdFq3Ts8sZN4XFix4el8I8NtvSzl48JCPWpE7LqtXg9iYOOI928Ccyb/wvw5NvcokxCayY9POLN8DmbXu3ILFc5dyIkD3Ef8lf2sImbU2CbgH6GfStTLGTAUwxlxpjFlojFnl+b96pqrljDEzjTFbjDEvnJppjOltjFnqyTx8aIxxejIQF3nmjT9HOacn27LeGLPOGPNoNiEvAKp41lHZE8MKY8yvxpganvmVjDGLjDHLjDFDM8VW0Riz3vN3IWPMBGPMWmPMt8aYJcaYBp5lRzxZjSVAE2PMFcaY+Z7nmWWMiTjX85+NMSbGGFPK83cDY8w8z9+DjTGfGmPmGWN2GmMeylTnVk+Ma4wxXxhjmgLdgNc9r11lz2t2naf8VZ73a51nnQUyPfeLngzWuvPFmtsiI8OIjYvPmI6LcxERGXbWMm63mz/++JOSnh8mDRpGsWz5bJYum8VDDw/C7XazceMWmjW7khIlLuGiiwrSoUNrypSN8F2j/qHSEaEkxCdmTCe59hEWEZqjusYYBgzuz4ghfysB6neRZcLZG3v6/Y+NcxEZGX7WMm63m8OH/8h4/89lzJgRLF82m2efeSR3g/aRsIhQXPEJGdMJ8UmERZT2Y0R5Lyy8NK5M20BifBJh4TnbBjKrXa8mQcFB7I2Jzc3wfCI0vBSJ8UkZ04mufYTm8HsAIKRACONmfMSnU0bTsmPz81eQC0JkZDixsd77wsgs+8LTZc7cFzZsGMXKlT+zfPls+vd/JqNDkx+FhpciKdM2kORKJvQffA+07d6G6Mm/5GZoFzzrg3/+8LdP4rfW7vQMITtzr7kZaGGtTfUMURoOXOtZdiVwOXAUWGaMmQb8BfQCmllrU4wx7wO3WGufNsb082RQMMZcll05YANQxlp7uafcJdmE2xVY5/n7I+A+a+02Y0wj4H2gDTASGG2t/dwYk3WwfboHgIPW2jrGmMuB1ZmWXQyst9Y+b4wJBuYD3a21+4wxvYCXgDvO8fwAjxpjenv+fspaO+sscZxSA2gNFAG2GGNGA9WAZz2vU7IxpoS19oAx5idgqrX2O8/rhOf/gsBY4Cpr7VZjzOfA/cDbnudIttbWN8Y8ADwO3HVmEMaYe0jv0BISXIKgoNw5+nUqxswyHz3yFDprmeXLVtOwQXuqV6/MRx+/yexZ89iyZQcjRnzAlKlfcuTIX6xbtwl36oX/ZZ5NM7O+FmfR6/Zr+G3OIq8fPvlBTt7/7Muce719butPfHwChQtfzMRvP6Z37+syjlzmF9m1+7wNz++ya/Lf3GGWKl2Sl0cN5pmHhuR4+7mQ5Og78Ry6Nrye5MT9lCkfwfsT32b7pp3E7Y4/f0Xxq3/+XZheZtmy1dSv35bq1aswZswIZs2ax4kT+TPz8G+3AYCSpUtwaY1KLJm3LLfCEj/6p1chy2aXQjFgnDGmKukXPQjOtCzaWrsfwBjzA9AcSAWuIL1DA3ARkN0vravOUm4KcKkx5l1gGpB58PPrxphBwD7gTmNMYaApMDHTRlDA838zTne0vgBezSaG5qR3dLDWrjfGrM20zA187/m7OukdtWjP8zgB13meH9KHkL2RzfOezTRr7QnghDEmCQgjvTP0nbU22RPngfOsozqwy1q71TM9DniQ0x2YHzz/rwCuyW4F1tqPSO+YcXGhirn2qyAuLoGyZSIzpsuUiSDB5f3RiPeUiY9LwOl0UrRoEQ4c8E6Jb9myg7/+OkbNWtVYtXIdn4+bwOfjJgAw+MUniItz5VbIeSYxfh/hmY64lY4IJSkhOUd161xxOfUb1eWGvtdQqNBFBIcEc/SvY4x8aXRehZsr4mJdlCt7+v0vWyYClysx2zJxcS6cTifFihXlwIGD51xvvCdzceTIX3z9zY80bBCV7zowCfFJRGTKRoVHliYxYZ8fI8p7ia4krwxsWGTpHG8DABcXvpjR40fwzisfsHbF+rwIMc8lufYRFnn6mGFYRCjJf+M1SE5MP98rbo+LlQtXU/3yqurA5ANxcS7KlvXeF7rO2BeeKhN3zn3hdo4ePUqtWtVZuXIt+VGSax+lM20DpSNKkZyY820AoE3XViyY8Vu+OHiZmwJjMHFWf/sqZMaYS0n/0X5mZ2MoMNeTEekKFMy07Mwft5b0TtC4TOd+VLfWDs7uKbMrZ609CNQF5pH+w3tMpjpPeMq2s9au97TzUKZ1RFlrLztHfNnFcDbHrbXuTOU2ZHqO2tba9jl4/uykcvr9KXjGssyHUNykd0RNDtqR2bnalPk5Tq3fZ1asWEPlKhWpUKEswcHBXHddV6ZN8z5Bfdr0aG7pnd7v7NnzaubPXwhAhQplM05ULFeuDNWqXcqe3elDRk5ddaps2Ui6devIxAk/+apJ/9iG1Zsof2lZypSPICg4iI492jJ/9m85qvvMgy/SscE1XN3wWkYMGcXUiTMu+M4LwLLlq6lSpRIVK5YjODiYG27ozpSp3idnT5k6mz59rgfg2ms7M3fe7+dcp9PpzBhWERQUROfObdmwYUveNCAPrV21gYqXlqNs+UiCg4Po0rMDc2bO93dYeWr9qk2Uv7QcZcpHEBwcxNU92jF31oIc1Q0ODuKdsa/y08QZzJ6Sf4eNbFy9mfKVyhJZLv17oF33q1gw+9yf+VOKFCtMcEj68cRiJYpRp2Ftdm2NycNoJbcsX77G67vw+uu7MnWq975w6tRoevdOP133mmuuZt689H1hxYrlMvaF5cuXoWrVyv9n787jZSz/P46/PnMcUfadVCQSydLyjZRSlG+RVi3ady2WVm0krb/2Tcu3Ukp7fYtWiXyVKHt2idBBjq2FcM7n98c9jnOOQ5szl5l5Pz3Ow7nvuWd8rjFzz3zu67o+FwsWLExsA7ajmZNmUrvurtTcrQYlMktw5PFtGf3JmL/0GO06t+XCUib0AAAgAElEQVTTNBs+lsr+0hdTM6sKPAk85u5eqEuvPLA4/vu5he7azswqAWuJJpWfTzSc7F0ze9Ddl8VvL+vuC4ANZpbp7huA4UUdRzQEbb27v2Vm3xENhyqSu6+xqDLZKe7+hkWB7+fuk4EvgNOAl4iGphVlNHAqMMKiil5NtnLcLKCqmbV09zHxIWUN3H3aNv79rZlP1PP0IZt7iLZlOPBO/HnK3jSEDPiZ6PkqbCZQx8z2cve5wFlEw9+Cy8nJ4epet/Luey+SkZHBiy++zowZc7j5lp5MmDCVD97/lBcGvs5/nn2AKVNHsnLlKs45+0oAWrU6kF5XX8bGjRvJzc2lR49byM6Orsy/PHgAlSpVZOOGjfTqeQurVq0J2cw/JScnh7tufIABrzxILCOD/74ylO9mfU+36y5k2qSZfP7JaBo324cHn7uLchXK0qZda7pdewEntun6xw++g8rJyaF7j5v54P3BZMRiDHzhNaZPn03fPtfwzfjJDB06jOeef5UXBj7CzOmjWblyFWd03VxedO7sryhXrgwlS5bk+E7H0OHY01mwYBEfvD+YzMwSZGRkMHz4//jPsy8HbOXfk5OTw2033MPANx4nFovx5uD3mDNrHj1uuJSpk6Yz/KNRNGneiAEv3E/58uVoe/RhdL/+Ujq0PiV06H9bTk4Od/S+j6dffYRYRox3XhnCd7O+54rrLmba5BmM+Ph/7NtsHx5+/l7KVSjL4e0P5fJrL+L4NqdzdKej2P/g5lSoWJ7OXY4F4Kar+jFz2pzArfprcnJyuPemh3hk8H1kZMR479UPmDd7Ppdcez4zJs9i1Cdf0KhpQ+59tj/lKpSldbtWXHLN+XQ54hzq1q9D73uuITc3l1gsxguPv8z3cxaEbtJ2d22fu/l64hRWrVrDkZ270u2Cszip445fqGVbcnJy6NHjFoYMGURGRgYvvPAaM2bM5tZbezF+/FTef38YAwe+xnPPPcS0aaNYsWIVZ599BRB9Fl5zTTc2bNhAbm4u3bvflPdZ+OKLj3LooS2pUqUic+eOpX//Bxg48LWQTf1DOTm5PHjzozww+B4yYhkMfe1Dvp89nwuvOZeZk2czetiXNGy6N3c924+y5ctwSLuWXHj1uXRtGy2ZUKN2darVrMbEMdv62pWaclN0JRj7ozGEZpZDNI8kk6hXYBDwgLvnmtnhwDXufpxF5YpfIBq29RlwlrvXMbNziSqX7UI0oX6wu98Wf+wuQG+inoYNwOXu/pWZ3UM0+XyCu59Z1HFEydDzbO6l6O3uH5rZQPLN+cjXjrrAAKBmvC2vunu/+P7BRMncW8DN7l7GzOrEH2dfM9sl3rYGwESiYWKnxeez/OLuZfL9O82AR4gSuhLAQ+7+zDb+/b7AL4WHkJnZocCzwFJgLHCAux9e+HiLCg0c5+7zzewc4FqiXpOJ7n6umR0CPEPUo3IycMum58fMjgTui8f5NXCZu/9uZvPj/95yi4oV3Ofuh7MN23MIWTLaq1ytPz4ohU1bkXpfiP6qPcpV/+ODUlipjMw/PijF7Zyx0x8flMK+nDIwdAjBla19eOgQgtq/8l5/fFCK+2LxZ380wiWhutU5tdi/nz0x//WEt/kPExjJK4+c6e7rzKweUW9HA3dfHzi0HYYSGCUw6U4JjBIYJTADQ4cQnBIYJTA7WgJzWQISmAEBEpiEzm1IYjsTDR/LJJo7cpmSFxERERGRxFMC8ye4+8/AAaHjEBERERH5s1J1DsxfrkImIiIiIiISinpgRERERERSkNaBERERERERCUw9MCIiIiIiKchTdA6MEhgRERERkRSkIWQiIiIiIiKBqQdGRERERCQFpeoQMvXAiIiIiIhI0lAPjIiIiIhICtIcGBERERERkcDUAyMiIiIikoJyXXNgREREREREglIPjIiIiIhICkrN/hf1wIiIiIiISBJRD4yIiIiISArKTdE+GPXAiIiIiIhI0lAPjIiIiIhICnL1wIiIiIiIiISlHhgRERERkRSUGzqAYqIERraL0VWahg4hqGdjpUKHENTRtfYIHUJw7/02N3QIQa3L2RA6hOBmrVwUOoSgytY+PHQIwf28aGToEILqun+v0CFImlACIyIiIiKSglSFTEREREREJDD1wIiIiIiIpCBVIRMREREREQlMPTAiIiIiIilIVchERERERCRpuGsImYiIiIiISFDqgRERERERSUEqoywiIiIiIhKYemBERERERFJQqk7iVw+MiIiIiIgkDfXAiIiIiIikIC1kKSIiIiIi8heZ2TFmNsvM5prZDUXc3svMppvZFDMbbmZ7bOvxlMCIiIiIiKSgXLzYf/6ImWUAjwMdgEbA6WbWqNBhE4ED3H0/4E3g3m09phIYEREREREpLgcBc919nruvB14Fjs9/gLuPcPff4ptfAbW39YCaAyMiIiIikoLcd4g5MLsCC/NtLwL+tY3jLwA+3NYDKoEREREREZG/xcwuBi7Ot+tpd386/yFF3K3IzMrMugIHAG229W8qgRERERERSUGJWAcmnqw8vY1DFgG75duuDfxY+CAzOwq4CWjj7r9v69/UHBgRERERESkuXwP1zayumZUETgPey3+AmTUHngI6ufuyP3pA9cCIiIiIiKSgHWEdGHffaGZXAB8DGcBz7j7NzPoB37j7e8D/AWWAN8wM4Ad377S1x1QCIyIiIiIixcbdPwA+KLTv1ny/H/VXHk8JjIiIiIhICvoz67QkI82BkR1e2TbN2WfEEzQa9STVu520xe2VTm7LvhNfZO8PH2TvDx+k8mntACjdqC4N3rmHhp8+SsOPH6ZCx9aJDn272KdNU24Z/iB9Rj5Mu8uO3+L2thccy03D7qf3h/dy5cs3U3HXKnm3HX/DGdz48X3c+PF9tDiuZSLD3q4atGnKtcPv57qRD3L4ZVv2KB985lH0/OgeenxwF5e90Ydqe+0KwM4VynDJKzdz+7TnOf62cxMc9fbT+oiWfPjlm3w89m0uuvKcLW4/4ODmvPXpIL79cQxHH9c2b3/DfRvw6gfPMmTUa7w7cjAdjm+XyLC3m8PatmLYV2/z2bh3ueSqc7e4/cCWLXj3s5eZtWQcx3Q8ssBtz7/2GBO/+5xnBj+coGi3n6PbH860b0cxc/porrv28i1uL1myJINfHsDM6aP5cvQQ9tgjWjahUqWKfPrJG6xaMZuHH+pf5GO/8/bzTJo4vFjj/6fatWvDlCkjmDZtFNdc022L20uWLMmgQY8zbdooRo16N6/9BxzQlLFjP2Ts2A8ZN+4jOnU6Ou8+Tz31f/zwwwTGjx+WsHYkws13PsBhx55G566Xhg6l2DRt05wHP3uchz8fwPGXnbjF7cde2In7P32Uez96iJsH96PKrlUL3F66TGkGjH2W8/pdlKiQpRgpgUkTZnaCmbmZNQwdy18Si7Fb/0v47pzbmHHkFVTsdCil6u+2xWGrhoxmVoeezOrQk+xXow+m3LW/s6DnQ8w86krmnn0btftcQEa5XRLdgn/EYsap/c7niXPvon+7Xuzf6RBqxL+cb7Jw+nzu7dibuzpcx8QPx9K595kAND6iObs1rsvd/76O+zrfxFEXd6RUmdIhmvGPWMw4od95PHvuPdzf7hqadWqVl6BsMvHdL3jwmOt56N+9+fypoXS85SwANvy+gY/vf4P373w5ROjbRSwW49Z7ruOi07tzXOtTOfbE9tRrULfAMVmLl9D7qtsY+vbHBfav+20d11/el46HdeGiLlfRu38vypYrk8jw/7FYLEbfe67n/C5XcvQhJ9HxxGPYq1D7f1yUxXVX9GXIWx9tcf9nHnuRq7vdkqhwt5tYLMYjD9/BcR270qTpEXTp0pl99qlf4JjzzzudlStX07BRax565BnuuvMmANatW0efvvdy3fW3F/nYnTt34Jdffi32NvwTsViMhx/uz/HHn0OzZkdy6qmdaNiwYPvPPbcLq1atpnHjw3j00f/Qv39vAKZNm0WrVsfxr391oFOns3nssbvIyMgAYNCgN+jU6eyEt6e4df53O558oOhkNRVYLMb5t1/CXef0o9dRV3JIp0PZtX7BdQ7nT5tH7+Ou5rpjejD2gy85s3fBiz2nXn0G08dOS2TYOwR3L/afEJTApI/TgdFElR+Sxs7N6vP7/CWs/2EpvmEjK4f8j/LtD/pT9/39+x/5fX4WABuXrmDj8tWUqFSuOMPd7uo024vlC5aSvXAZORtymDDkS/Zrf2CBY+aMmcaGdesBmD9xDhVqVAagRv3azBk7g9ycXNav/Z1FMxawT5umCW/DP7Vbs71YvmAJK+LPweQhY2jc/oACx/z+y9q830vuvFPeCXXD2t+Z/80sNvy+PqExb0/7tWjMD98vZNGCxWzYsJEP3hnGkccULI+/eGEWs6fPxXMLfpDMn/cDC76P1g5btnQ5K5avoFLligmLfXto2mJfFny/iIXx9g9952OO6nB4gWMWL8xi1vQ55OZuWTD0y/+N49cd/Mt6UQ46sDnffTef77//gQ0bNvD66+/SqePRBY7p1LE9gwa9AcBbb71P2yOiXubfflvLF19+zbp1W1Yh3WWXnenZ/WLuvGvH7pE68MBmBdr/xhtD6NixfYFjOnZsz0svvQnA229/wBFHHALA2rXryMnJAaBUqZ0KfMEaPXocK1euSlArEueAZk0oX65s6DCKzV7N6rN0fhbLFi4lZ8NGvhwymgPbFVwHcdqYb1kf/yycM3EWlWtWzrut7r71qFClAlNGTUpo3FJ8lMCkATMrAxxCtLLpafF9MTN7wsymmdlQM/vAzE6O37a/mX1uZuPN7GMzqxkq9pI1KrP+x+V52+uzssmsXnmL4yr8uyUNP36YOk9eT2bNKlvcvnPT+lhmCX5fsKRY493eylevxMofs/O2V2ZlU7761r+Atjz1CKaPjE7Qi2csoNHhzcgsVZJdKpalQcvGVCziudnRla9ekdX5noPVWdmUK+I5aHlWO67//CH+fcMZvNf3hUSGWKyq16hK1uKledtLspZSvWbVbdyjaE2aNyIzM5Mf5i/anuEVu+o1q5L14+b37ZIfl1G9ZrWAESVGrV1rsHDR5mUSFi3OolatGls9Jicnh9Wr11D5DxLUfn2v44GHnuK339Zu87jQatWqwaJ87V+8OItatapv9ZicnBzWrPk5r/0HHtiMCRM+5ZtvPuHKK2/MS2gkOVWqUYnsrM3fBbKzsqlYo9JWjz+iy1FMGjkBADPjrJvP46U7U+dz4a/IxYv9JwQlMOmhM/CRu88GVphZC+BEoA7QBLgQaAlgZpnAo8DJ7r4/8BxwR4igga2s3VrwzbL606+Z1uoiZh7dnZ9HT2aPB7oXuL1EtYrs8VBPFlzzyBb33dHFSwkWtJUmHNi5NbvvV4/hT0el1Wf+bwrTR0zk6rdv57xHruL7CXPITcYP8T/5HIwZNIx72vTgg7sH0/bKE4o/rkQpov1/tcu+arXK3Pt4P27s3i9Yd//fVfR7ILna8HcU1e7C/3dFH7P1x2zatDH19qrDu+9uOdRuR/P32x8d8/XXk2jR4igOOaQj1157OTvttFPxBCoJYUV9GdjKa731CW2o12Qv3nvqHQDan92BSSPGF0iA0okn4E8IqkKWHk4HHor//mp8OxN4w91zgSVmNiJ++97AvsCw+IdDBpBV1IOa2cXAxQA3VdyPk8rU2e6Br8/KpmStzb0GJWtWZsOyFQWOyVn1c97v2YM/YdcbNo9vjpUpTb3nbyHrvpf4beLs7R5fcVu1JJuKtTb3OFWsWZnVy1ZucdzehzTh6CtO5KEufdm4fmPe/o8ff4ePH49O4uc+fCXLvi/yv3KHtnrJCsrnew7K16zMmiKeg00mDxnDCf0vSERoCbE0axk1d9185blGzeosW/LnP4h3KbMLTw5+iIfuGsDk8d8WR4jFasmPy6iZr+ehRq1qLF3yU8CIEmPxoix2q10rb7v2rjXJylpa5DGLF2eRkZFB+fLlWLFi6++Ng/+1Py2aN2Hu7K8oUaIE1apVZviwNziy3SnF1o6/a/HiLGrna/+uu9YkK2tZkccsXryEjIwMypUry4oVBYeHzZo1l99++43GjfdmwoQpCYldtr/sJdlUzjeCoHLNyqxcumKL45ocsh8nXnEyfU+9Oe+zsEGLvWl4YCPandWBUruUokRmCdb9uo5X7hmUsPhl+1MPTIozs8pAW+A/ZjYfuBboQtF9G8T3T3P3ZvGfJu7evqgD3f1pdz/A3Q8ojuQF4LfJc9ipbk1K7lYNyyxBxY6HsnrYuALHlKi2echE+XYHsW5uNETGMkuw5zO9WfH2CFa9/2WxxFfcFkz+jqp1alC5dlUyMjNo0bEVU4Z9U+CY2o3rcNqdF/LUhffyS/aavP0WM3apEE3YrtVwd2o13IOZ/0u+D/BFk7+jSp0aVIw/B007tmT6sPEFjqlSZ/MX3IZtm5M9P7mGCm7L1InT2WPP3dl191pkZpbg3ye047OPR/2p+2ZmluCxgf/Hu69/wMdDduyKU1szZeI06uy5G7Xj7T/uhKMZ/tHnocMqdl9/M4m99qpLnTq7kZmZyamnHs+QoZ8UOGbI0E8466wo+TjppGMZMfKLbT7mU0+/yO519mevBgfT5ojOzJ4zb4dMXgC++WZygfafckpHhg4tWDls6NBhdO16MgAnnvhvRo6MzvN16uyWN2l/9913pX79eixYsDCxDZDt6rvJc6hRtyZVd6tGRmYJWnVszTeFvgvUaVyXC+/qxr0X3Mma7NV5+x/t/iCXt7qIK1tfzEt3DGTU2yPSKnnJdS/2nxDUA5P6TgZedPdLNu0ws8+B5cBJZvYCUBU4HBgMzAKqmllLdx8TH1LWwN3DlO7IyWXRLU9Tb1BfLCNG9mvDWTd7ITV6ncFvU+eyZtg4qp53HOXbHQQbc9i46hcWXB1NTq1w3CGUOagxGRXKUunkqLTsD1c/wtrp3wdpyt+Rm5PL67c+x+Uv3ohlxPjq9ZEsmbOIY3uewg9T5zH10/F07t2VnXYuxQVP9ARg5eLlPHXR/5GRWYIeb9wGwLpf1vJCz0fJzdlykvOOLjcnl3dvHciFL/YmlhHj69dHsnTOItr3PJlFU79n+qfjaXVOe/Y6pAm5GzeydvWvvHb1gLz73zD6EUqVKU1GZgkatz+A/5x1F8vmLg7Yor8mJyeH22+4l2dfe4RYRgZvDX6PubPmceX1l/DtpBmM+HgU+zZrxGMD76Vc+XIc0b41V1x3CR0P68Ixx7fjgJbNqVCpPCecdhwAva+6jZnfJk9vZE5ODrfdcA8D33icWCzGm4PfY86sefS44VKmTprO8I9G0aR5Iwa8cD/ly5ej7dGH0f36S+nQOvpi/uqQZ9mzfh122aU0o6d8SO/u/fjfiDGBW/XHcnJy6N7jZj54fzAZsRgDX3iN6dNn07fPNXwzfjJDhw7juedf5YWBjzBz+mhWrlzFGV03lxqeO/srypUrQ8mSJTm+0zF0OPZ0ZsyYE7BFf01OTg49etzCkCGDyMjI4IUXXmPGjNncemsvxo+fyvvvD2PgwNd47rmHmDZtFCtWrOLss68AoFWrA7nmmm5s2LCB3Nxcune/iezsqGfqxRcf5dBDW1KlSkXmzh1L//4PMHDgayGbul1c2+duvp44hVWr1nBk5650u+AsTipU9CGZ5ebk8tytz3Dji32IZWQw8vVPWTRnIaf0Op15U+Yy/tOv6XrjuZTauRQ9n7gOgOU//sT/XXhn4MiluFiyjYeWv8bMRgJ3u/tH+fZdBexD1NtyGDAb2Al4wN2HmVkz4BGgPFGS+5C7P7Otf2fi7sen9Qvp2Vip0CEEtbM6c3nvt7mhQwhqQ24Szq/azhasWfrHB6WwErGM0CEE9/OikaFDCKrr/r1ChxDcawv+u7URLkEcuuuRxf797H+Lhye8zeqBSXHufngR+x6BqDqZu/8SH2Y2Dpgav30SUWIjIiIiIrJDUQKT3oaaWQWgJHC7u6fOxAERERGRNBeqzHFxUwKTxorqnRERERER2ZEpgRERERERSUGp2gOjmbciIiIiIpI01AMjIiIiIpKCUrXasHpgREREREQkaagHRkREREQkBWkOjIiIiIiISGDqgRERERERSUGuHhgREREREZGw1AMjIiIiIpKCVIVMREREREQkMPXAiIiIiIikIFUhExERERERCUw9MCIiIiIiKUhzYERERERERAJTD4yIiIiISApK1TkwSmBERERERFKQFrIUEREREREJTD0wIiIiIiIpKFeT+EVERERERMJSD4xsF7ewMXQIQfXLWRc6hKB65P4SOoTg9ilVI3QIQW0gN3QIwZ1QZu/QIQQ1ZsPS0CEE13X/XqFDCOql8Q+EDkEK0RwYERERERGRwNQDIyIiIiKSgjQHRkREREREJDD1wIiIiIiIpCDNgREREREREQlMPTAiIiIiIilIc2BEREREREQCUw+MiIiIiEgK0hwYERERERGRwNQDIyIiIiKSgjQHRkREREREJDD1wIiIiIiIpCDNgREREREREQlMPTAiIiIiIinIPTd0CMVCPTAiIiIiIpI01AMjIiIiIpKCclN0DowSGBERERGRFOQqoywiIiIiIhKWemBERERERFJQqg4hUw+MiIiIiIgkDfXAiIiIiIikIM2BERERERERCUw9MLLDa9Fmfy7uezGxjBifvPoJbz7xRoHbO1/YmfanH03OxhzWrFjNQ9c8xE+LfwLgthf7sXfzvZn+zXT6nXdbiPD/sXKHN6d234sgI0b2K8NY+sRbBW6vdEpbdr3pXDYsyQbgp4EfkP3qMADqDerDLs0b8OvXM/juvP4Jj317OejwA7mq3+XEYjHef+UDXn781QK3N/1XE6687XL23GdPbuvWn8/fHwVA81bNuKLvZXnH7V5vd27r1p/RH3+R0Pj/qeZtWnBB34uIZcT49NVhvP3EmwVu73Th8Rx1evv4e2ANj13zMD8t/ok6jepy6R3dKF12Z3Jzcnjzsdf5YsjoQK34+9L9HFDY3m2acvytZxPLiDH2tRGMGPBegdtbnnkUrc5qR25uLut/Xcebvf/D0rmLA0W7ffzr8APp0e8KYrEYQ175gJcef6XA7U3/tR/db7ucevvsSZ9utzMyfg5o0aoZV/Xtlnfc7vV2p0+32/lfkp0DAJq2ac65fS4klhHjs1eH8e6AtwvcfuyFnWh7Wru888CT1z7K8vj7AKB0mdI8MPwxxn38Fc/f+kyiwy92N9/5AKO+GEelihX470tPhg5nh5Gboj0wSmACMrPawONAI6LesKHAte6+fhv3udHd70xQiMHFYjEu638ZN595M9lZy3lwyIOMHfYVC+cszDvmu2nz6HlsD35f9zsduv6b8248n3svvweAt596i51K78QxZ3YI1YR/JhZjt/6XMOeMPmzIymbvofexetg41uVrP8DKIaNZdMvTW9x92ZPvECu9E1XOPDpREW93sViMnndcRa/Tr+OnrJ94+oMnGP3JGBbMWZB3zNLFy7iz572cdukpBe478ctJXND+EgDKVijLK6Nf5OvPv0lo/P9ULBbj4v6X0vfMW8jOyubeIQ8wbthYFuV7DcybNo9rju3F+nW/c3TXDpx943ncf/m9rF/7Ow/3fICs+VlUrF6J+95/kImfT+S3Nb8GbNFfk/bngEIsZpzQ7zye7nonq5dk0/29O5g+bHyBBGXCu18w5uVPAWh01P50vOUs/nPO3aFC/sdisRhX39GdHqdfy7Ksn/jPBwMY/cmXzC9wDljKHT3v4fRLTy1w3wlfTuLc9hcD0Tng9dGDGJdk5wAAi8U4//ZLuOPMPmQvyeau9/6Pbz4dx+I5i/KOmT9tHr2Pu5r169bTrusxnNn7HB6+4r6820+9+gymj50WIvyE6PzvdpxxUiduvP2+Pz5Ykp6GkAViZga8DfzX3esDDYAywB1/cNcbizu2HUmDZg3Imv8jS39YwsYNGxk1ZBQHtz+4wDFTx0zh93W/AzBr4kyq1KySd9vkLyaz9pe1CY15e9qlWX1+n7+E9T8sxTdsZOV7/6N8+4P+9P1//mIKOUncfoB9mjdk8fzFZP2QxcYNGxn+7ghaH92qwDFLFi1l3ox5eO7WrzQdfuxhjB0xLu+1kizqN6tP1vwslv6wlI0bNjJ6yCgOav+vAsd8O2Yq6+Ptmj1xFpVrVgbgx+9/JGt+FgArl65g9fLVlK9ULrEN+IfS/RxQ2O7N9iJ7wRJWLFxGzoYcJg0ZQ+P2BxQ45vd87S25806Q5Fdg92nekEXzF/Nj3jngMw4t4hzw3Yx5eG7uVh/niGMP46skPAcA7NWsPkvnZ7Fs4VJyNmzkyyGjObBdwfPAtDHfsn5ddP1zTr7zAEDdfetRoUoFpoyalNC4E+mAZk0oX65s6DB2OJ6APyEogQmnLbDO3Z8HcPccoCdwvpl1M7PHNh1oZkPN7HAzuxsobWaTzOzl+G1nm9kUM5tsZoPi+/Yws+Hx/cPNbPf4/oFmNsDMRpjZPDNrY2bPmdkMMxuY799rb2ZjzGyCmb1hZmUS9qwUUrlGZX76cXne9vKs5VSuXnmrx7fv0p7xI5Lv6trWZNaozPp87d+QlU1mjS3bX7FDS/b55GHqPnk9mfm+vKWCKjWqsOzHzcMgfsr6iao1/nobjzz+CD59d8T2DC0hKtWozPJ8r4HsrOxtvgeO6tKOCSPGb7G/ftP6ZGaWYMmCJcUSZ3FJ93NAYeWrV2TVj9l526uysilfveIWx7U6qx03fP4Qx91wBv/t+0IiQ9zuqtaowrIfl+VtL8taTtUaVf/y4xx1fFuGvfvZ9gwtYSrVqER2VsHzQMUalbZ6/BFdjmLSyAkAmBln3XweL92Z3K8DkfyUwITTGCjwLcPd1wA/sJWhfe5+A7DW3Zu5+5lm1hi4CWjr7k2B7vFDHwNedPf9gOiw7S8AACAASURBVJeBR/I9TEWi5KknMAR4MB5LEzNrZmZVgJuBo9y9BfAN0Gt7NPhvMdti19YuJh5+whHstV993nrqraIPSEZbNn+LJ2D1sK/5ttVFzGjfnZ9HT6bOg92LuFPyKuIl8JerqlSuVok9G9Zl3Mivt1NUiWNFvgeKbn+bEw6n3n578d+nCo6Nr1itIt0f6sWj1zycfBVp0v0cUNiffD6+HDSMu9v04P27B3PUlSckILDi81feA1uz6RwwNgnPAQBW1IfBVp6C1ie0oV6TvXjvqXcAaH92ByaNGF8gAZL04e7F/hOC5sCEYxR9+tna/qK0Bd509+UA7r4ivr8lcGL890HAvfnuM8Td3cymAkvdfSqAmU0D6gC1iebkfBH/0CgJjCmyAWYXAxcDNKm4L7uX2f1Phv3nZWctp2qtzVfbq9Sswopl2Vsc17R1M7pc0YUbTr2ejes3bvc4QtmQlU3JfO3PrFmZDUtXFDgmZ9XPeb8vH/wJu/Y+O2HxJcJPWcupVmvz1daqNauyfOmWr4FtOaLj4Yz6cDQ5G3O2d3jFLjtrOVXyvQYq16zMimUrtjhuv9ZNOfmKU7n51N4F3gOly5Tmpuf7MPi+l5g9cVZCYt6e0v0cUNjqJSuoUGtzD1SFmpVZs2zlVo+fNGQMJ/a/IBGhFZtlWT9RrVa1vO1qNauwfOlf+zLeNonPAQDZS7KpXLPgeWDl0i3PA00O2Y8TrziZvqfenPc+aNBibxoe2Ih2Z3Wg1C6lKJFZgnW/ruOVewYlLH6R7U09MOFMAwoMXDazcsBuwGoK/t+U2spj/NlkJ/8xmwb/5ub7fdN2ifhjDov38jRz90buXuSnn7s/7e4HuPsBxZG8AMyePJtadXel+m7VKZFZgsM6HsbYYWMLHLNn4z254q4ruP2CfqzOXl0scYTy6+Q57FSnJiV3q4ZllqBip0NZPWxcgWNKVNs8fKR8+4NYN3dR4YdJajMnzaR23V2puVsNSmSW4Mjjj+CLT778S49xZOcjGJ6Ew8cA5kyeQ826tagWfw+07ngYXxd6DdRtvCeX3XU5d15we4H3QInMEtzwzE2MfPszvnw/+aougc4BhS2c/B1V6tSgUu2qZGRm0KxjS6YNKzhksEqdGnm/79O2OcvnJ9ewwcK2PAe0ZfQnRV5X26p2ndvyaZIOHwP4bvIcatStSdXdqpGRWYJWHVvzTaHzQJ3Gdbnwrm7ce8GdrMn3Pni0+4Nc3uoirmx9MS/dMZBRb49Q8pJGcvFi/wlBPTDhDAfuNrOz3f1FM8sA7gcGAvOAS80sBuwK5J+1vcHMMt19Q/wx3jGzB90928wqxXthvgROI+p9ORP4K3VTvwIeN7O93H2ume0M1Hb32f+wvX9Lbk4uT94ygH6DbieWEWPYa8P4YfYPnNmrK3OmzmHcsLGcf9MFlNq5FDcM6A3ATz/+xO0X9APgnjfvoXa93Si1SykGjn2BR659mAmjJoRoyt+Tk8vCW55mr5f6Yhkxsl8bzrrZC6l59Rn8NmUuq4eNo9p5x1G+3UF4Tg45q35hfq+H8+7e4K072alebTJ2KcW+455lwbWP8fPnE8O152/IycnloZsf5b7B9xCLxfjgtQ+ZP3sB519zLrMmz+KLYWNo2HRv+j97G2XLl6FVu5acf/U5nNM2yrtr1K5OtZrVmDRmcuCW/D25Obk8c8uT9Bl0G7GMGMNf+5SFs3/g9F5nMnfqHL4eNo5zbjqPUjuX4toBNwDRe+CuC/pzyHGtaXRQY8pWKEvbk48E4JGrH2L+9O9DNukvSftzQCG5Obm8c+tALnqxN5YR4+vXR7J0ziKO7nkyC6d+z/RPx3PIOe2pf0gTcjZuZO3qX3n16gGhw/5HcnJyefDmR3lg8D1kxDIY+tqHfD97Phdecy4zJ89m9LAvadh0b+56th9ly5fhkHYtufDqc+na9nxg8zlgYpKeAyD6f3/u1me48cU+xDIyGPn6pyyas5BTep3OvClzGf/p13S98VxK7VyKnk9cB8DyH3/i/y5Mm6KlXNvnbr6eOIVVq9ZwZOeudLvgLE7qmLwVOGXbLOnGQ6cQM9sNeAJoSNTj8gFwDbAeeAloBnwLVAf6uvtIM7sH6ARMiM+DOQe4FsgBJrr7uWZWB3gOqAL8BJzn7j/EJ+oPdfc348cMdfd947Hkv60tcA+wUzzUm9294EIDhRy3+7Fp/ULq5+l9LaBH7i+hQwiucsbOoUMIagNbr/6ULhrGkqvC2/Y2ZsPS0CEEV7tEer8GXhr/QOgQgsussmdRs1eDqVKuQbF/P1u+ZnbC26wERrYLJTBKYNKdEhglMEpglMAogVECowQmMdL7W5eIiIiISIrKTdGOCk3iFxERERGRpKEeGBERERGRFJSqU0XUAyMiIiIiIklDPTAiIiIiIiko1DotxU0JjIiIiIhICtIQMhERERERkcDUAyMiIiIikoJURllERERERCQw9cCIiIiIiKQgT9FJ/OqBERERERGRpKEeGBERERGRFKQ5MCIiIiIiIoGpB0ZEREREJAVpHRgREREREZHA1AMjIiIiIpKCVIVMREREREQkMPXAiIiIiIikIM2BERERERERCUw9MCIiIiIiKUg9MCIiIiIiIoGpB0ZEREREJAWlZv8LWKp2LUl6MbOL3f3p0HGElO7PQbq3H/QcqP3p3X7Qc5Du7Qc9B+lCQ8gkVVwcOoAdQLo/B+neftBzoPZLuj8H6d5+0HOQFpTAiIiIiIhI0lACIyIiIiIiSUMJjKQKjXfVc5Du7Qc9B2q/pPtzkO7tBz0HaUGT+EVEREREJGmoB0ZERERERJKGEhgREREREUkaSmBERERERCRpKIERSWJmtoeZHRX/vbSZlQ0dk4RhZhXNbL/QcYRgZhlmVsvMdt/0EzomEREpPiVCByDyd5nZKcBH7v6zmd0MtAD6u/uEwKElhJldRLRgVyWgHlAbeBI4MmRciWRmDYABQHV33zf+Bb6Tu/cPHFpCmNlIoBPRuXwS8JOZfe7uvYIGlkBmdiXQB1gK5MZ3O5DSyZyZbfP/2N0fSFQsocXPA9cCe5Dve427tw0WVAKZWXXgTqCWu3cws0ZAS3d/NnBoCWFmOwNXA7u7+0VmVh/Y292HBg5NipF6YCSZ3RJPXloDRwMvEH2ZTReXA4cAawDcfQ5QLWhEifcM0BvYAODuU4DTgkaUWOXdfQ1wIvC8u+8PHBU4pkTrTvRlpbG7N4n/pHTyElf2D37SyRvABOBmokRm00+6GAh8DNSKb88GegSLJvGeB34HWsa3FwFpcRErnakHRpJZTvzvY4EB7v6umfUNGE+i/e7u680MADMrQXTlOZ3s7O7jNj0HcRtDBRNACTOrCZwK3BQ6mEAWAqtDB5Fo7n5b6Bh2IBvdPZ0uXhVWxd1fN7PeAO6+0cxy/uhOKaSeu3cxs9MB3H2tFfpQkNSjBEaS2WIze4roivM9ZrYT6dWr+LmZ3QiUNrN2QDdgSOCYEm25mdUjnriZ2clAVtiQEqof0ZXX0e7+tZntCcwJHFOizQNGmtn7RFdhgdQfQmVmj2zrdne/KlGx7ACGmFk34B0KvgZWhAspoX41s8psPg8eTHol9evNrDSb21+PfK8DSU1ayFKSVnzc6zHAVHefE78S3cTdPwkcWkKYWQy4AGgPGNEX2f94Gr2p41/YnwZaASuB74Ez3X1B0MAkYcysT1H7U72HwszWA98CrwM/Ep0D8rj7CyHiCsHMvi9it7v7ngkPJgAzawE8CuxL9JqoCpwcH1Kb8uIX8G4GGgGfEA2tPtfdR4aMS4qXEhhJavH5L/Xd/XkzqwqUcfeiPsxSmplVAmqnywcW5CVwJ8eHTuwCxNz959BxJZKZ3Us01nst8BHQFOjh7i8FDUyKXfyK+ylAF6Jhk68Bb7n7yqCBSRDxIcR7EyWys9x9Q+CQEir+fjiYqP1fufvywCFJMVMCI0krfuX1AKIJvA3MrBbwhrsfEji0hCiqAhWQbhWoRrn7YaHjCMXMJrl7MzM7AegM9ARGuHvTwKEVOzN7yN17mNkQipj75e6dAoQVhJntCpwO9AKud/dBgUNKKDPLBC4DNp0LRgJPpcuXeDM7sYjdq4lGJyxLdDwhxCtQ1qFgFbq3gwUkxU5zYCSZnQA0J6o+g7v/mGbroJR39zVmdiFRBao+ZpY2PTBxw8zsGqKrz79u2plGY98z43//G3jF3Vek0dzVTV/S7wsaRWDx4UOnA+2AD4HxYSMKYgDRe+GJ+PZZ8X0XBososS4gqsA1Ir59OPAV0MDM+qV6QmtmzxGVTZ9GwVLqSmBSmBIYSWbr3d3NbNPEvV1CB5RgqkAF58f/vjzfPgfSYuw70eTlmURDyLrFh1GuCxxTQrj7+Pjfn4eOJQQzuw04DpgBvAr0dvd0qsCX34GFeh0/M7PJwaJJvFxgH3dfCnnrwgwA/gWMYnOyn6oOdvdGoYOQxFICI8ns9XgVsgrxRR3PJ1oXJF1sqkD1RbpWoHL3uqFjCMndbzCze4A17p5jZr8Bx4eOKxHMbCrbKBueBmvB3EJUga1p/OfOeO+bEU1gT/X255djZvXc/TvIK+6RTmWE62xKXuKWAQ3iPbLpMIxujJk1cvfpoQORxNEcGElq8eojeVW43H1Y4JAkgczs7KL2u/uLiY4lhHglvl5EK1BfnE4rUJvZHtu6PdUr0aV7+/MzsyOJFjOcR/RZsAdwnruP2OYdU4SZPQHsTrSgJ8BJRIs5XgsMdfcjQsWWCGZ2GNESAkuIyienYxKfdpTAiCQpM6tNVDrzEKIr0aOB7u6+KGhgCWRmj+bbLAUcCUxw95MDhZRQZvYa0ZyHs9193/haCGPcvVng0CQAM6sCZKdTKfVN4uuAbarCNdPd02YdkPiijScCreO7soGa7n751u+VOsxsLtGFnKlsngOTVkl8OtIQMkk6Zjba3Vub2c8UHEKy6apLuUChJdrzwGCiUqoAXeP72gWLKMHc/cr822ZWntQf751f2q9AXeg8UJJoMvevqX4eiC9WeDewArid6HVfBYiZ2dnu/lHI+BLBzNq6+2dFVOGqZ2ZpU4UqPhf0O6I5L6cSrYf1VtioEuoHd38vdBCSWEpgJOm4e+v43+lUcawoVd39+XzbA82sR7Bodgy/AfVDB5FAab8CdeHzgJl1Bg4KFE4iPQbcCJQHPgM6uPtXZtYQeIVoXaBU14ao7R2LuC3lq1CZWQPgNKIqdNlE1Rgt1YeMFWGmmQ0mGkaWd/5LlwQ2XSmBkaQVvwI5bdPihWZWBmjs7mPDRpYwy82sK9GXFdj8IZY2Cq0BEiNaifn1cBElXB+iL6q7mdnLxFegDhpRYO7+XzO7IXQcCVDC3T8BiJfK/QrA3WemSyecu/eJ/9qv8ALGZpYOBT5mAv8DOrr7XAAz6xk2pCBKEyUu7fPtS/kENt0pgZFkNgBokW/7tyL2pbLzia7CPkh0sv6SzWWF00X+NUA2AgvSaQ6Quw8zswlsXoG6e7qtQF1o+FCMaHHbdJgDkpvv97WFbkuH9uf3Flue998E9g8QSyKdRNQDM8LMPiIqp50e2Ws+7n5e6Bgk8ZTASDKz/JNV3T3XzNLmNe3uPwBps9r4VnwDrI3/3zcAWpjZ0nRZgTuuFLCS6HzeKD72f1TgmBIp//ChjcB80qOUdFMzW0P0hbV0/Hfi26XChZU48eFyjYHyhRLZcqTBc+Du7wDvxNdA6wz0BKqb2QDgnU09dKlOBW3Sk6qQSdIys7eBkUS9LgDdgCPcvXOwoBLIzF4gOkmvim9XBO5397TphTGz8cChQEWilae/AX5z9zODBpYg8TVgulBoBWp3T/fEVtKAmR1P9MW9E5B/EvfPwKvu/mWQwAIys0pEhV26uHvb0PEkgpkNIypos6mAS1fgTHdPm4I26UgJjCQtM6sGPAK0JbrqMhzo4e7LggaWIGY20d2b/9G+VGZmE9y9hZldCZR293vT6Tkws1nAfulUMrYwM7sX6E80jOojokUde7j7S0EDk4Qxs5buPiZ0HBKGmU0qXDq+qH2SWmKhAxD5u9x9mbuf5u7V3L26u5+RLslLXCze6wLkXXlLmyF0cWZmLYEzgffj+9LpOZhHVDY4nbV39zXAcUSL9zUgWsBP0selZlZh04aZVTSz50IGJAm13My6mllG/KcraVbQJh2l0we9pBgzqwpcBNQh32s5jYZQ3Q98aWZvxrdPAe4IGE8IPYDeROO9p5nZnkBarL4d9xswycyGU7B86FXhQkq4TQncv4FX3H1FulThkjz7bRpKC+DuK80sLXphBVBBm7SkIWSStMzsS6ISkuOBnE373T1tFvAys0ZEQ+gMGO7u0wOHFIyZxYAy8avxacHMzilqv7u/kOhYQjGzu4nmQawlWv+lAjDU3f8VNDBJGDObDBzu7ivj25WAz929SdjIRKS4KIGRpJXuY1zNbPei9serk6WF+OJllxIlsOOJFvV7wN3/L2hgCWJm+7v7+EL7Orr7kFAxhRAfSrnG3XPMbGegnLsvCR2XJIaZnU3UE1ugN9rdB239XpIqVNAmPSmBkaRlZv2BL939g9CxhGBmU9m83kNpoC4wy90bh4sqsTYlsWZ2JtGaD9cD4919v8ChJUR8DZhz3H1qfPt0ognsadX7YGat2HIo6YvBApKEM7PGwBGoNzrtqKBNetIcGElm3YEbzex3YAPRB5e7e7mwYSVG4eERZtYCuCRQOKFkmlkm0RCix9x9g5ml01WZk4E34wlca+BsCq5GnfLMbBBQD5jE5qGkDiiBSS8z2bweEma2ezr1Rqe5mJlVLDSEUN9vU5z+gyVpuXvZ0DHsSNx9gpkdGDqOBHuKaOHCycAoM9sDSJs5MO4+z8xOA/4LLCSqyFV4VfZUdwDQyDWcIG3Fy6j3AZYSJbFGlMSmRU+sFCho48CpwJ1hQ5LipiFkktTiY13rk2/V5XRZhdzMeuXbjAEtgMrufnSgkHYIZlbC3TeGjqM4FRo+CFANWE28Elm6DKEDMLM3gKvcPSt0LBKGmc0F/uXuKp2bplTQJv2oB0aSlpldSDSMrDbR8JGDgTFEJ7F0kL8HaiPROihpU4ENwMyqE11pq+XuHeIfYi2BZ8NGVuyOCx3ADqQKMN3MxlGwlHSncCFJgi0kSuAlDZnZIHc/C5hexD5JUeqBkaQVvwp9IPBVfCJ3Q+A2d+8SODRJEDP7EHgeuMndm5pZCWBiupRPNbODgWnu/nN8uyzRcKqxYSNLHDNrU9R+d/880bFIGGb2LLA30UWc/EnsA8GCkoQxswnu3iLfdgYw1d0bBQxLipl6YCSZrXP3dWaGme3k7jPNbO/QQRU3MxtCweFDBaTZlecq7v66mfUGcPeNZpbzR3dKIQOIhg5u8msR+1KaEhUBfoj/lIz/SBqIn/dvBEqb2Rqi4WMA64GngwUmCaEERpLZIjOrQDSBeZiZrQR+DBxTItxXxL5NCU26LUH+q5lVJt7+eI9EOg0lsfyT1909N94LlfLM7GeKTuTTqhqhgLvfFjoGSTx3vwu4y8zucvfeoeORxNIQMkkJ8WEk5YGP3H196HiKk5kdD9R298fj2+OAqkRf5q539zdCxpdI8dLRjwL7At8SPQ8nu/uUoIEliJm9DYwk6nUB6AYc4e6dgwUlkmBmNoIikll3T5f5kGnNzA4ran+6FPRJV0pgJKnFx7pWp+ACdild+9/MvgBOc/eF8e1JwJHALsDz7n5kyPgSxcxiRIUbxhGNfzeihTw3BA0sgcysGvAIUeEKB4YTLWS5LGhgIglkZvvn2ywFnARsdPfrAoUkCRQfVr1JKeAgogWNlcCmsLQYaiCpqVDt/9z47nSo/V9yU/ISNzpePjTbzHYJFVSixYdL3e/uLYFpoeMJIZ6onBY6DpGQ3H18oV1fmJnmRqUJd++Yf9vMdgPuDRSOJIgSGElm3YG907D2f8X8G+5+Rb7NqgmOJbRPzOwk4O10WsjQzK5z93vN7FGKHjpzVYCwRIKIr7y+SQzYH6gRKBwJbxHRsGJJYUpgJJmla+3/sWZ2kbs/k3+nmV1CNJwqnfQiGjq30czWkT4TuGfE//4maBQiO4bxRIm8Ea2J9T1wQdCIJGEKXciJAc2ByeEikkTQHBhJWula+z8+7+G/RG2eEN+9P7AT0Nndl4aKTUREJJHM7DIggyiJWQ187+5fhI1Kipt6YCSZpWXt//i8h1Zm1hZoHN/9vrt/FjCshIoncTcCewFTgLvdfU3YqBLPzBoA1wB1KFjIQpNXJeWZ2Z3ufmP893buPix0TJI48ZLxdwLnE30XMGA34DkzG5dOBV3SkXpgRCTpmNlHRMNGRgHHAWXd/dygQQVgZpOBJ4mei7wFPIuY1CyScvKvwF54NXZJfWb2IFAW6OnuP8f3lSNaK22tu3cPGZ8ULyUwkrS2siL9aqJ5AU+5+7rERyWJYGaT3L1Zvu20/PJiZuPdff8/PlIk9SiBSW9mNgdoULiAS3x5hZnuXj9MZJIIGkImyWweUdWtV+LbXYhKKjcAngHOChSXFD8zs4pEQwYAMvJvu/uKYJElQL6qS0PMrBvwDgXngaV0+0XiqplZL6L3/abf86T6fEjBi6o+6e45Zqar8ylOPTCStMxslLsfVtQ+M5vm7o23dl9JbmY2n2jtHyviZnf3PRMbUWKZ2fdsrrpUWMq3XwTAzPps63Z3vy1RsUjimdl/iUrov1hof1fgVHfvFCYySQQlMJK0zGwGcLS7/xDf3h34yN0bmdlEd28eNkKR4mFmLd19TOg4RERCMbNdgbeBtWwupX0gUBo4wd0XBwxPipmGkEkyuxoYbWbfEV2Jrgt0i69G/0LQyKRYmdk2x7q7+4Rt3Z4CHgc03l+EvGp8A4Dq7r6vme0HdHL3/oFDk2IUT1D+la8ipwEfuvvwsJFJIqgHRpKame0ENCQ6cc3UxP30YGYj4r+WAg4gWrTMgP2Ase7eOlRsiaAeRpHNzOxz4Fqi4i3N4/u+dXetxi6SotQDI0nLzHYmWol9D3e/yMzqm9ne7j40dGxSvNz9CAAzexW42N2nxrf3JVoXJdXVNbP3tnajxn5LmtnZ3ceZFZgStjFUMCJS/JTASDJ7nmjca8v49iLgDUAJTPpouCl5AXD3b82s2bbukCJ+Au4PHYTIDmK5mdUjXlbfzE4GssKGJCLFSQmMJLN67t7FzE4HcPe1VugSnKS8GWb2H+Aloi8vXYEZYUNKiJ/d/fPQQYjsIC4HngYamtli4Huic4GIpCglMJLM1ptZaTZfdatHvrUwJC2cB1wGbFpxeRTRZN5UNz90ACI7CnefBxwVL+AS27Qqu4ikLk3il6RlZu2Am4FGwCfAIcC57j4yZFySWGZWEtibKJGd5e4bAoeUUGbWCqhDvgtShddFEEllZlYduBOo5e4dzKwR0NLdnw0cmogUEyUwkpTiQ8VqA78BBxNVoPrK3ZcHDUwSyswOJyqZPZ/oNbAbcI67jwoYVsKY2SCgHjAJyInvdne/KlxUIollZh8SzYm8yd2bmlkJYKK7NwkcmogUEyUwkrTMbLy77x86DgnHzMYDZ7j7rPh2A+CVdHldxBdzbeQ6kUsaM7Ov3f3A/OXFzWySu6dDQQ+RtBQLHYDIP/CVmR0YOggJKnNT8gLg7rOBzIDxJNq3QI3QQYgE9quZVWbzfMiDgdVhQxKR4qQeGElaZjadaO7DfOBXoiFE7u77hYxLEsfMniP60jIovutMoIS7nxcuqsSJL+jZDBhHvgIWWgdG0omZtQAeBfYlSuqrAie7+5SggYlIsVECI0nLzPYoar+7L0h0LBKGme1EVEK1NVECOwp4wt3TohqdmbUpar9KLEu6MLMY0TzIcUQXtIw0LOYhkm6UwEjSMbNSwKXAXsBU4Fl316rLaSrdq5CJpDszG+PuLf/4SBFJFZoDI8noBeAAouSlA1qRPG3Fq5DNAR4DngBmm9lhQYNKADMbHf/7ZzNbk+/nZzNbEzo+kQT7xMxO0kLGIulDPTCSdMxs6qbymPFymePcvUXgsCSAdK9CJiJRIg/sAmwE1rF5PmS5oIGJSLFRD4wko7whQho6lvbSugqZmV1QxL67Q8QiEoq7l3X3mLuXdPdy8W0lLyIprMQfHyKyw2mab5iMAaXj27rqln6+MbNnKViFbHzAeBLtZDNb5+4vA5jZE0CpwDGJJFS8Cllhq4EFusglkpo0hExEkpaqkFlp4D3gOaL5YCvcvUfYqEQSy8y+AloQzYsEaAJMBioDl7r7J6FiE5HioQRGRCTJmFmlfJtlgXeB0cCtAO6+IkRcIiGY2avA7e4+Lb7dCLgWuB14292bhYxPRLY/JTAiknTMbCrxVbeLkuqLmZrZ90Ttt0J/A+DuewYKTSThzGxS4SRl076ibhOR5Kc5MCKSjI4LHUBgXYCF7p4FYGbnACcB84G+4cISCWKWmQ0AXo1vdyEqqb4T+Yq+iEjqUA+MiKQEM6sCZHsanNTMbAJwlLuviK978ypwJdAM2MfdTw4aoEgCxeeCdWPzXLjRROtCrQN2dvdfAoYnIsVACYyIJB0zOxi4G1hBNM59EFCFqDT82e7+UcDwip2ZTXb3pvHfHwd+cve+8W0NmRERkZSmIWTy/+3dX6jfdR3H8efr7Mw2xT9rnYlEgVqxGUga40BetTGhLrqIlHBI5EVXkQbdLEQkWYuIECsGFQz1YlCQRFcdmRAJTiGHokyK0l1EfxwsXKY257uL7+/EcegEx/f74fM7zwcczr7f783raocX3/f785V69GPg28DlwGPA56rqaJLtwGFgrgsMsCHJ4uyI2N3A19Y88/91rQtJflFVt77bTty878JJ65l/6CT1aHH1aNQk36mqowBV9UKStsmmcRj4XZKTwGvA7wGSfIzh+xfSenDn7Pd634mT1h0LjKQevbXm36+d+xsEdAAABW1JREFU82zu52Kran+SI8BVwMqavZ8Fhl0Yae6tHmJRVSdaZ5E0LXdgJHUnyVngVYaF3c3Af1YfAZuqamOrbJKmkeQ05z9O/bIJ40iakG9gJHWnqja0ziCpraq6FIYxUuDvDId5BNjL8IFXSXPKNzCSJKlbSZ6squX3uidpfiy0DiBJknQBzibZm2RDkoUke4GzrUNJGo8FRpIk9ew24FbgH7OfW2b3JM0pR8gkSZIkdcM3MJIkqVtJPpHkSJLnZtfXJ7m7dS5J47HASJKknv0M2AecAaiqZ4EvN00kaVQWGEmS1LOLq+qpc+692SSJpElYYCRJUs9OJrmW2Uctk3wJ+FvbSJLG5BK/JEnqVpJrgJ8CnwFOAS8Ce6vqRNNgkkZjgZEkSd1LcgmwUFWnW2eRNC5HyCRJUneSLCd5Jsm/kzwBfNTyIq0PFhhJktSjnwDfArYCPwTubxtH0lQsMJIkqUcLVfVoVb1RVb8ElloHkjSNxdYBJEmS3ocrknzx3a6r6lcNMkmagEv8kiSpO0kOnedxVdUdk4WRNCkLjCRJkqRuuAMjSZK6leTOJJdl8PMkTye5uXUuSeOxwEiSpJ7dUVWvADcD24CvAt9rG0nSmCwwkiSpZ5n9/jxwqKqeWXNP0hyywEiSpJ79IckKQ4H5bZJLgbcaZ5I0Ipf4JUlSt5IsAJ8C/lJV/0qyFfhwVT3bOJqkkfgGRpIk9ayA64BvzK4vATa1iyNpbL6BkSRJ3UpykGFkbFdV7UiyBVipqp2No0kayWLrAJIkSRdguapuTHIMoKpOJbmodShJ43GETJIk9exMkg0Mo2QkWcIlfmmuWWAkSVLPHgAeAbYl2Q88DhxoG0nSmNyBkSRJXUuyHdjN8P2XI1V1vHEkSSOywEiSpG4lebiqbn+ve5LmhyNkkiSpZ59cezHbh/l0oyySJmCBkSRJ3UmyL8lp4PokryQ5Pbv+J/DrxvEkjcgRMkmS1K0kB6pqX+sckqZjgZEkSd1KsgDcBlxdVfcl+QhwVVU91TiapJFYYCRJUreSHGT47suuqtqRZAuwUlU7G0eTNJLF1gEkSZIuwHJV3ZjkGEBVnUpyUetQksbjEr8kSerZmdnJYwWQZInhjYykOWWBkSRJPXsAeAS4Msl+4HHgu20jSRqTOzCSJKlrSbYDu2eXj1XV8ZZ5JI3LHRhJktS7i4HVMbLNjbNIGpkjZJIkqVtJ7gEeBD4IfAg4lOTutqkkjckRMkmS1K0kx4Ebqur12fVm4Omq2tE2maSx+AZGkiT17CVg05rrDwB/bhNF0hTcgZEkSd1J8iOGnZc3gOeTPDq73sNwEpmkOeUImSRJ6k6Sr5zveVU9OFUWSdOywEiSJEnqhiNkkiSpW0k+DhwArmPNLkxVXdMslKRRucQvSZJ6dgg4CLwJfBZ4CHi4aSJJo7LASJKknm2uqiMMY/EnqupeYFfjTJJG5AiZJEnq2etJFoA/Jfk68FdgW+NMkkbkEr8kSepWkp3AceAK4D7gcuD7VXW0aTBJo7HASJIkSeqGI2SSJKk7Se6vqruS/IbhA5ZvU1VfaBBL0gQsMJIkqUerJ439oGkKSZNzhEySJHUtyRJAVb3cOouk8XmMsiRJ6k4G9yY5CbwA/DHJy0nuaZ1N0rgsMJIkqUd3ATcBO6tqa1VtAZaBm5J8s200SWNyhEySJHUnyTFgT1WdPOf+ErBSVTe0SSZpbL6BkSRJPdp4bnmB/+/BbGyQR9JELDCSJKlH/32fzyR1zhEySZLUnSRngVff6RGwqap8CyPNKQuMJEmSpG44QiZJkiSpGxYYSZIkSd2wwEiSJEnqhgVGkiRJUjcsMJIkSZK68T8oDiHcr0luPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203eb706e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = diabetes.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里关于数值型变量和类别型变量的相关性还需要研究一下"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "feature = diabetes.loc[:,diabetes.columns != 'Outcome']\n",
    "label = diabetes['Outcome']\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(feature,label,test_size=0.2,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 568 entries, 519 to 738\n",
      "Data columns (total 8 columns):\n",
      "Pregnancies                 568 non-null int64\n",
      "Glucose                     568 non-null float64\n",
      "BloodPressure               568 non-null float64\n",
      "SkinThickness               568 non-null float64\n",
      "Insulin                     568 non-null float64\n",
      "BMI                         568 non-null float64\n",
      "DiabetesPedigreeFunction    568 non-null float64\n",
      "Age                         568 non-null int64\n",
      "dtypes: float64(6), int64(2)\n",
      "memory usage: 39.9 KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.798\n",
      "Test set score: 0.732\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression().fit(X_train,y_train)\n",
    "print(\"Training set score: {:.3f}\".format(logreg.score(X_train,y_train)))\n",
    "print(\"Test set score: {:.3f}\".format(logreg.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "训练集准确度：0.781\n",
    "\n",
    "测试集准确度：0.771\n",
    "\n",
    "默认参数下模型在训练集上准确度为78%，在测试集上准确度为77%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.38489998 0.53382232 0.38883851 0.4499787  0.49065977]\n",
      "cv logloss is: 0.44963985431339265\n"
     ]
    }
   ],
   "source": [
    "#交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss = cross_val_score(logreg, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正则化的 Logistic Regression及参数调优\n",
    "这部分用交叉验证GridSearchCV、LogisticRegressionCV任意一种方式均可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic回归的需要调整超参数有：C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "目标函数为：J = C* sum(logloss(f(xi), yi)) +  penalty \n",
    "\n",
    "在sklearn框架下，不同学习器的参数调整步骤相同：\n",
    "设置候选参数集合\n",
    "调用GridSearchCV\n",
    "调用fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4492971836695937\n",
      "{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "选出最佳正则系数和正则函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203ec0b4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #plt.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.00\n",
      "Accuracy on test set: 0.65\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train,y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train,y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集准确度：1.00\n",
    "\n",
    "测试集准确度：0.65\n",
    "\n",
    "\n",
    "\n",
    "这个模型过拟合比较明显，虽然在训练集中有一个完美的表现，但是在测试集中仅仅有65%的准确度。\n",
    "\n",
    "\n",
    "\n",
    "SVM要求所有的特征要在相似的度量范围内变化。我们需要重新调整各特征值尺度使其基本上在同一量表上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.77\n",
      "Accuracy on test set: 0.77\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train_scaled,y_train)\n",
    "\n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train_scaled,y_train)))\n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test_scaled,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据的度量标准化后效果大不同！现在我们的模型在训练集和测试集的结果非常相似，这其实是有一点过低拟合的，但总体而言还是更接近100%准确度的。这样来看，我们还可以试着提高C值或者gamma值来配适更复杂的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提高了C值后，模型效果确实有一定提升，测试集准确度提至77%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RBF核SVM正则参数调优\n",
    "RBF核是SVM最常用的核函数。\n",
    "RBF核SVM 的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和核函数的宽度gamma\n",
    "C越小，决策边界越平滑； \n",
    "gamma越小，决策边界越平滑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "gammas = [0.0001,0.001, 0.01, 0.1, 1]\n",
    "#gammas =[1e-5, 1e-6]\n",
    "param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7869718309859155\n",
      "{'C': 1000, 'gamma': 0.0001}\n"
     ]
    },
    {
     "data": {
      "image/png": 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gb0fqYP+KIN8gpsRNYUrclLZjTdYmcipyOuxANh7YSKO1EYBAn8A238eEaLspa1T4KKcnHxpyTYCH+CscxUOd4UpZKBROxmKzsK1wG+emnOtx9nuHyB94foWf3o8J0RM6ONktNgv5lfkddiD/zfsvGw7YAzp8db6kR6TblUfLLmRM5BgCfAL6LUdWjpkxw0KIDe0mPNiTOdkZbnimxRn+OEy/yt4z3IXOcKUsFAon86PpR6qbq73TBAX2Eh8xYyDMuXZ+H50PoyNHMzpyNAtHLQTs0VnHqo+x37yffWX72G/ez2dHP+Nfh/4FgF7oSQ1PbVMg46LGMS5qHCF+Ib2u12ixsuNwGYtnjnTq93A5HTLDd9nrTmU9C9mtmeErXeIMV8pCoXAyBqMBndAxK36Wu0XpO5YmOJJlf5p1ATqhszvUw5I5P/V8wJ5EV1xb3KY89pftJ7swmw9yT9QhTQ5L7uADGR81noiAiA5z7z5SQaPF1rmFqjeTeNpJzvA34Pt1MP5iuHStpo5wpSxcwObNm7npppuwWq0sW7aM1as7tiNvbGxk6dKl7Nq1i+joaDZu3EhKSgpms5nf/va37Nixg6uuuornnnvOTd9A0RcMRgOTYyYT7j/AUtjuwLgTmuvcWpJcCEF8SDzxIfGcM/KctuOldaXsL9vPT2U/sd+8nx9NP7L58InKywnBCSeisKInkHHQD52AWWlR7vga2nKyM9zarHnElFIWGtNaonzLli0kJSUxY8YMFi5c2KHqbPsS5Rs2bGDVqlVs3LiRgIAAHnjgAfbs2cOePUOnI5c3U95Qzl7zXpZPWe5uUfpHXgYgIGW+uyXpRGxQLLFBsSxIOuF4r2ysPOEDadmFfHnsy7bEwrAxYazO/KgtjHdc9DgSghO805fUFa3OcBeglIXGDKREeXBwMPPnzycnJ8cdoiv6QXZhNhLZwV9R+d8P8U1MJGjaVDdK5iD5GRB/KgRGulsShwj3D2d2/Gxmx5+ozlzbXMu3xXu5Zv37jEuupLiukKzCLKz2RpyE+YUxPno804dN5+pJV+On9+7y665iyCiLx7Y/xk9lPzl1znFR41g1c1WPYwZSojwmZhDZWocIhkIDEf4RbVFANQYDhX/+M/j6kvj4Y4RdcIGbJeyBploo2AFzVrhbkgER7BtMU20yjWVzuf3Xs5iXHkODpYFD5Yfsu5Cy/ewz72PNd2vILszm72f9ncgA71CO7mTIKAt3MZAS5QrvwiZtGIwG5iTMQa/TY62poejOu/BLTUUfFYXxlluxlJURdblrnMd95kg22CyDoiS5IceMn4+O05LtSiDAJ4BTYk/hlNhT2sZsyt/EnZl3suSjJaw5Zw1pEV0XZlTYGTLKorcdgFYMpES5wrs4WH4Qc4O5zQR1/LHHsZSUkLJ+Hf5jx2K85VZKHngQq9lMzI03et4DQf5XoPeDkXPcLcmAMeSYmJ4cSYBv91niF6ReQEJIAn/64k/8/uPf87cz/8bchLkulNK78O7aw15A+xLlTU1NbNiwgYULF3YY01qiHOhQolzhXWQaMwGYmzCXmkwDFe+8Q/TVfyDw1FPRBQSQ9MzThP/m15ief4Hie+5FtvRT9xjyMiBpJvgFuVuSAWGqaeSn4mqHQmZPjT2V9b9Yz/CQ4Sz/bDlvH3jbBRJ6J0NmZ+EuBlKiHCAlJYWqqiqampr4z3/+w6efftrBOa7wHAxGA+OixhFlDSDvzjvxGzWKmBtvbDsvfHyIf/BBfKJjML/0EtbyMhL+9jd0/h6QXVxXZm87fOZf3C3JgMluKUk+x8H+FQkhCay9YC23b72dB7Y9QH5lPrdNv80ltau8CaUsXMCFF17IhRde2OHY/fff3/Y+ICCAd955p8trDx8+rKVoCidR21zLd8e/Y+nEpZQ89hiW48dJ2bC+kyIQQhB3y834xERT8vAjHFt2LUnPr0Ef6qJ2n91x+GtAujW/wllk5ZoJ9fdhcqLjeS7BvsE8c9YzPLnrSdbuW8uRqiM8vuBxhzLFhwrKDKVQOIFvir7BIi2cURBK5bv/IvqaawicPLnb8VFLl5LwxBPUffstR65YSvPx492OdQl5GeAXYs8Q9nKyck3MSovCR9+325tep+f2Gbdz1+y7yCrM4opNV1BYU6iRlN6HUhYKhRMwGA3EWAIJ+d+1+I9OJ+bGlb1eE37xRYx48UWajh7lyJLLaXLnLjI/A5Lnek0/6O4oKK/jiLluQFVmLx17KS/87AVKaktY/NFivi/93okSei9KWSgUA0RKiaHQwI2ZIVhNZuIffgSdn2OJXiHz55H8+j+w1dRweMnl1O/Zq7G0XVBpBHPOoAiZzcqx+ysGWg9qTsIc3vzFmwT7BnP15qvZlL/JGeJ5NUpZKBQD5EjVEeK+O8b47CKir11G4CmT+nR94OTJJK9bhwjw5+jSpdRmZ2skaTe0liQfBP4KQ66JmBA/xgwbuK8hLTyNty58i0kxk7h96+288N0LXeZEDRWUslAoBsi2g59x/SYbulEpxCzvX00o/7RUUtavxzcxkWPXXU/VJhc+yeZvhaBoiHNNz2etkFKSlWtmzqgYp4WeRwZE8vLPX2bhqIU8//3zrPp6VVsDp6GGUhYKxQDxe+4twutg5GN/c9j81BW+w4aR/OZaAiZPxnjLrZS99ZYTpewGKe3O7ZTT7X0TvJic4zWUVjcyz8GQWUfx0/vx4LwHuWnaTWzK38TVn1yNqd7k1DW8Ae/+3+ElbN68mbFjx5Kens6jjz7a6fzWrVuZNm0aPj4+vPvuu26QUNFfyj77lPHfFHPo4skEThr4k7k+PJyRr75CyFlnUfLAg5Q+84y2pg9zDlQXDg4TVI79Bq5F/wohBMtOWcZTZz7FwbKDXP7R5RwqP+T0dTwZpSw0prVE+aZNm9i3bx/r169n3759HcaMHDmS119/nSVLlrhJSkV/sFZUUHTP3RyOg+g/Xu+0eV2a7Z33lf3nIHBuG3LNjIgKZESUdhnoP0v+Ga9f8DoWm4UrNl3B1oKtmq3laWiqLIQQ5wshDgghcoQQq7s4/5QQ4ruW10EhREW7cyOFEJ8KIfYLIfYJIVK0lFUr2pco9/PzaytR3p6UlBQmT56MzsvNAEON4ocfhooqXrrYn+lJs3u/oA+0ZntHX3cdFW+/jfF/bsbWqIGtPD8DwkdAlHcX0bNYbWzLMzNvACGzjjIxeiLrfrGOkaEjufGLG3lr/1tDwvGtWQa3EEIPrAHOBQqAHUKID6SUbY/VUsqb242/EWhf8P8N4CEp5RYhRAhgG4g8xQ8/TON+55Yo9x8/juF33NHjGEdKlCu8j+ovvqDqg//y5c+iiTl1PEG+zn+a1Tzb22aF/K9h3C8077KmNXsKq6husDhc4mOgDAsexuvnv85fvv4Lj25/lPzKfFbNXIWvzrvzVHpCy0fZmUCOlDJPStkEbAB+2cP4xcB6ACHEBMBHSrkFQEpZI6Ws01BWzVDlxwcflvJyiu65B/2YUbwyrYL5idp2ldMs27v4R2ioGBQmqKxcu79iIMl4fSXIN4inznqKP0z6AxsPbGTFZyuoaqpy2fquRsvaUInAsXafC4AuO9gLIZKBVOCLlkNjgAohxHstxz8DVksp+2247W0HoBWOlChXeBclDz2MtbyCvLsWYS160SVlrcMvvgh9RAQFN93EkSWXM/LVV/BLTh7YpK35FakLeh7nBWTlmBk7LJTYUNcWZdQJHbecdgupYancn30/V3x8Bc+d8xwjQkf0frGXoeXOoqvH5+4Me4uAd9spAx/gdOA2YAaQBlzVaQEhrhNC7BRC7CwtLR24xBrgSIlyhfdQtWULVR9+SMwNf+Rzv1ziguJIj0h3ydohp88/ke29eAn1eweY7Z2XATFjISzeOQK6iYZmKzsOlzE33TUmqK64ZPQlvPTzlzA3mFny0RJ2lexymyxaoaWyKADaq9ckoLuqXItoMUG1u/bbFhOWBfgPMO3ki6SUL0kpp0spp8fGxjpJbOfSvkT5+PHjufTSS9tKlH/wwQcA7Nixg6SkJN555x2uv/56Jk707uSowYqlvJzie+/Df8J4IpZdzbbCbcxPnO9Ss2KHbO8rBpDtbWmCo9mDImR299FyGi02lzi3e2LG8Bm8deFbRPhHsOzTZXyQ+4Fb5XE2WpqhdgCjhRCpgBG7QugUGyqEGAtEAtknXRsphIiVUpYCZwM7NZRVU3orUT5jxgwKCgpcLZaij5Q88CDWqipGvvYqeysPUN1c7ZbOaq3Z3seWXcux664n4YnHCTv//L5NUrADmusGjQlKJ2Bmmvu7SyaHJfPmhW9y61e38tfMv3K48jArp65EJ7w/0lGzb9CyI1gJfALsB96WUu4VQtwvhGhvh1kMbJDtPMEt5qjbgM+FED9iN2m9rJWsCkVvVH3yKVUff0zs8hsIGDuWTGMmOqFjdrxzQ2YdpS3b+5RTMN58C2Xr1vVtgvytIHSQoq1z3hVk5ZqYnBRBWIBnRCKF+4fzwrkv8JvRv+HlH1/mtozbqLfUu1usAaNp8yMp5cfAxycdu/ukz/d2c+0WoPuGAAqFi7CUlVF8330ETJhA9LJlgL0k+eSYyYT7O95gx9now8MZ+dqrGG++hZL7H8BqMhNz40rHzGL5GRB/KgRGai+ohlQ3NPN9QSV/PMOz8kR8db7cM+ce0sLT+NvOv1FYU8gzZz9DXFCcu0XrN96/N1IoNKb4gQewVlcT/+gjCF9fyhvK2Wvey9xE15ugTkYXEEDSs8+0ZHs/T/G99/We7d1YYzdDDYKQ2e35ZVht0u3+iq4QQrB04lKeOfsZ8irzWPzRYvab97tbrH6jlIVC0QNVmzdTvWkzsStWEDBmDADZhdlIJPMTPMOE05btfe21VGzc2Hu299FssFkGhXPbkGPGz0fHtGTP3SGdOeJM1l6wFp3QceXmK/ni6Be9X+SBKGWhUHSDxWym+L77CZg0iehl17QdNxQaiPCPYEL0BDdK1xEhBHG33sKwv6ymessWjl17Hdbq6q4H530Fej8Y4R5/izPJyjUxPTmSAF+9u0XpkbFRY1l34TpGhY/if778H17f87rXlQhRykKh6AIpJcX33Y+tpoaERx5G+NjdezZpw2A0MCd+Dnqd592goq680p7tvXs3R65YiqWr/KP8rZA0E/y0K7jnCkw1jfxUXK1JlVktiA2K5R/n/4Nzk8/lyV1Pcm/2vTRbm90tlsMoZeECrr76auLi4pg0qW8d1BTuo3rTJqo//ZSYG2/Ef/TotuMHyw9ibjAzL3GeG6XrmfCLL2LECy/QdPQoh5dcTtORIydO1pXZy3wMAhNUdq69hepcF9WDcgYBPgE8ccYTXD/5et479B7Xf3Y9lY2V7hbLIZSycAFXXXUVmzdvdrcYCgexmEwU3/8AAZMnE331HzqcyzRmArglv6IvtGV7V1fbe3u3ZnvnbwXkoHBuZ+WaCPX34ZRE90Wk9Qed0LFy6koeOf0Rvjv+HZd/fDmHKw+7W6xeUcrCBSxYsICoKPcnDCl6x25+ug9bXV0H81MrWYVZjI0cS2yQZ1YMaI892/sthL8fR5deSe22bfaQWb8QSOxUEMHrMOSYmZUWjY/eO29jF6VdxKvnvUp1UzWXf3w524u2u1ukHtE0z8KT+Prtg5iO1Th1zpgRIZx+6RinzqlwL1UffUz1ls+Iu+1W/EeN6nCutrmWb0u+ZenEpW6Sru/4p6WdyPa+9joSzhKEzZoHes9IYOsvx8rqOFpWx1VzU9wtyoCYGjeVty58i5Wfr+T6Lddz5+w7+c2Y37hbrC7xTpWsUGiApbSUkgceIPDUU4n6wx86nf+m6Bss0qJ5SXJn05btPX4Mxk8bKcv33DBTR2n1V3iLc7snkkKTWHvhWmbFz+Le7Ht5cueTWG0adUYcAENmZ6F2AIqekFJSdO992OrriX/kYYS+c6RTVmEWQT5BTImd4gYJB4Y+PJyRt16M8S+7KHlzK9awZx3P9vZADLkmYkL8GTMsxN2iOIVQv1CeO+c5Ht/xOK/vfZ3DVYd57PTHNGmq1V/UzkKhAKo+/JCazz8n9qab8E/rXDpCSkmmMZOZ8TPx9VITjs6YRdLPBeGXXOJ4trcHIqUXmHJMAAAgAElEQVQkK9fM3FHRXqvsusJH58Mds+7gLzP/wtaCrSzdtJTi2mJ3i9WGUhYuYPHixcyZM4cDBw6QlJTEq6++6m6RFO1oPn6c4gcfInDKFKKuurLLMUeqjmCsMXpM1nafkRLyMxCjFhD/8EOOZ3t7IIeO11Ba3cg8N/av0JIl45ew5pw1GGuMLP5oMXtMe9wtEqCUhUtYv349RUVFNDc3U1BQwDXXXNP7RQqXIKWk+J57kQ0N3ZqfwJ61DXhEPah+YToE1UWQekbfsr09EEOO61uoupr5ifNZe8Fa/PX+XLX5Kj49/Km7RVLKQjG0qfrgA2q+/JLYm/8H/9TUbscZjAaSw5K9t11mFy1U7dnej9uzvZde2XW2tweSlWtmRFQgI6I8x56vBemR6bx14VuMjxrPrRm38tIPL7m1RIhSFoohS3PJcYofepjAadOIuuKKbsc1WhvZUbyDeQmem7XdK/kZED4Cojr6Y8IvvpgRLzxP0+HD9mzvo0fdJKBjWKw2tuWZPbLKrBZEB0bzynmvcFHaRTz77bPckXkHTdYmt8gy6JWFtxXrcgXqd9Jifrr7bmRTEwkPP9St+Qlgd8luGqwNHl3io0dsVsj/2p613YVDOOT0009key9eQsO+fW4Q0jH2FFZR3WBh7iAImXUUf70/D89/mJVTVvJh3ocs+3QZZQ1lLpdjUCuLgIAAzGazujm2Q0qJ2WwmICDA3aK4lcr/vE9NRgZxt9yMX0pKj2MNRgO+Ol+mD5vuGuGcTfEP0FDRYz2owFNPbcv2PnLFUnu2twdywl8xOJ3b3SGE4PpTr+eJM55gn3kfSz5aQm5FrktlGNR5FklJSRQUFFDqJbZYVxEQEEBSUpK7xXAbzSUllDz8MIHTTyPy97/vdbyh0MBpw07zqJj3PpHX2V/RFf5paaSsW8exa1uyvZ94grDzz3OBgI6TlWti3PBQYkL83S2KWzg/5XwSgxO58Ysb+f3Hv+fJM550WdDFoFYWvr6+pPbgtFQMPaSUFN11F7K5mYSHHkLoet5cF9cWk1ORw6/Sf+UiCTUgPwNix0Ho8F6H+g4fTvKbb3LshuUYb74Za/ldRC5e7AIhe6eh2crOw+UsmTXS3aK4lVNiT2H9L9az8ouVLP98OatnrmbRuEWarzuozVAKxclUvvdvard+Tdytt+KXnNzr+KzCLMDzq8x2i6UJjmT3uqtojz48nJGvvkLIGWdQfN/9lD77nEeYcncfLafRYhsyzu2eiA+J540L3mB+4nwe+uYhHvnmEWzSpumaSlkohgzNRUWUPPIIQTNmEHn5EoeuyTRmEhcUR3pEusbSaUTBDrDU97kkuS4wkKTnniX817/GtGYNxfe5P9s7K8eMXieYlaYqOAME+wbz9FlPs3TCUhqtjQi0zWYf1GYohaIVu/npbqTVSvzDvZufACw2C9uKtnFu8rneW1YiPwOEDlL6nnkufHyIf+hBfKKjML/8CtaychKeeBydv3v8BYZcE5OTwgkN8M5yK1qg1+n584w/Y5M2zf+Pqp2FYkhQ+a9/UZuZSdxtt+I3wrHEuj2mPVQ3VXuvCQrszu34KRAY0a/L7dnetxK3ehXVn37KsWuvw1Zb62Qhe6e6oZkfCiqVCaobdEL7W7lSFopBT3NhISWPPErQzJl9ctZmGjPRCR2z42drKJ2GNNaAcadTWqhGX3WVPdt7+3bMbqht9k1eGVabHHIhs56EUhaKQY2UkqI770JK6bD5qZWswiwmx0wm3N+72na2cTQbbJY+Obd7Ivziiwk991zK3liLtdK1faOzcs34++iYluz9vTi8FYf+coQQk7QWRKHQgoq336E2K4thf74Nvz7klpQ3lLPHtMd7CwcC5H0Fej8Y4bydUcyK5dhqaih7Y63T5nSErFwT01MiCfDtPtNeoS2OPma9KITYLoRYLoTon/FToXAxzUYjxx97jKDZs4m47LI+XZtdmI1Eem9JcrA7t0fMAj/nJRMGjBtH6Lk/o+yNN7BWVTlt3p4w1TTyU3H1oK4y6w04pCyklPOBy4ERwE4hxDohxLmaSqZQDAApJYV33glA/IMP9sn8BPas7Qj/CCZET9BCPO2pNUPxj30OmXWEmOXLsVVXu2x3kTWIWqh6Mw7/BUkpDwF3AquAM4BnhBA/CSF+3d01QojzhRAHhBA5QojVXZx/SgjxXcvroBCi4qTzYUIIoxDiOce/kkIBFRs3Upe9jbjbb8cvKbFP19qkDYPRwJz4Oeh1Xmr2OLzV/tMJzu2TCRg/npBzzrHvLlzQByMrx0Sovw+TEsI0X0vRPY76LCYLIZ4C9gNnAxdLKce3vH+qm2v0wBrgAmACsFgI0eExTUp5s5RyipRyCvAs8N5J0zwAZPTh+ygUNBUYOf74EwTPnUPEZZf2+fqD5QcxN5i9t8osQP5W8AuBhKmaTB+z/AZsVVWUrdV+d5GVa2ZWWjQ+ehWP404c/e0/B+wGTpVSrpBS7gaQUhZi3210xUwgR0qZJ6VsAjYAv+xhjcXA+tYPQojTgGGA+1tEKbwGabNRdOedIATxDzzQr0Qlg7GlK56351ckzwON+oUHTpxIyFlnUfbPN7DW1GiyBsCxsjqOltUN2haq3kSvyqJlh3BMSrlWSll/8nkpZXePFonAsXafC1qOdbVGMpAKfNHyWQc8Cfy5N/kUivaUb9hA3bZtxK26Hd/EvpmfWjEUGhgbOZbYoFgnS+ciKgugLFcTE1R7YlaswFZZSfmbb2q2RlauvSS58le4n16VhZTSCkQLIfz6OHdXj3TdVSNbBLzbshbAcuBjKeWxbsbbFxDiOiHETiHETlWGXNF07BjH//YkwfPmEfG73/VrjtrmWr4t+da7TVBtJcm1VRaBkyYScuaZlP3jdaw12mR1G3LMxIT4MzouRJP5FY7jqBnqCGAQQtwlhLil9dXLNQXYo6daSQIKuxm7iHYmKGAOsFIIcRj4G7BUCPHoyRdJKV+SUk6XUk6PjfXSp0CFU5A2G0V3/BWh0xH/YP/MTwDbi7ZjkRbmJ3p5yGxQDMRpH8kVs2IF1spKyt96y+lzSynJyjUzd1S099bmGkQ4qiwKgQ9bxoe2e/XEDmC0ECK1ZVeyCPjg5EFCiLFAJJDdekxKebmUcqSUMgW4DXhDStkpmkqhaKV83Xrqduxg2OpV+MbH93seQ6GBIJ8gpsROcaJ0LkRKu3M7dQH0MVy4PwSeMongMxZQ9tprTt9dHDpeg6mmUfkrPASHqs5KKe/r68RSSosQYiXwCaAHXpNS7hVC3A/slFK2Ko7FwAbpCQXzFV5J09GjHH/ySYJPP53w3/ym3/NIKck0ZjIzfia+GjmGNcd0CKqLnFbiwxFiV6zg8KWXUb5uHTHXXeu0eU+0UFX+Ck/AIWUhhIgFbgcmAm3Nm6WUZ/d0nZTyY+Djk47dfdLne3uZ43XgdUfkVAw92sxPPj7EP3D/gMwVR6uPYqwx8oeJf3CihC4mv8VfobFzuz2BkycTfPrplL32GlGXL0EXHOyUeQ05ZkZGBTEiykvb2Q4yHN2nvgX8hD1i6T7gMHYzk0LhVsrffIu6nTsZtno1vsN7bxvaE5nGTADvrwcVPhIiXdtOOHbFcqwVFZSvX9/7YAewWG18k2dWJigPwlFlES2lfBVollJmSCmvBry0brNisNB05AjH//d/CT5jAeG/vmTA8xmMBpLDkhkR6li/C4/DZoXDX0PaAnCxQzhwyhSC58/H/No/sNXVDXi+H42VVDdamKNMUB6Do8qiueVnkRDiF0KIqdijmxQKtyBtNgrv+CvC15f4+wdmfgJotDays2Qn8xK8OGS26HtoqITUM92yfMyK5VjLyihfv2HAc7XWg1L9KzwHR5XFg0KIcOBW7NFJrwA3ayaVQtEL5WvXUr9rF8PuuAPfYcMGPN/ukt3UW+q9O78iv6UeVOrpblk+aOpUgufOxfzqqwPeXWTlmhg3PJSYEPe0cFV0xtGqsx9KKSullHuklGdJKU9rF82kULiUxvx8jv/vU4SceSbhv+qpgozjGIwGfHW+TB823SnzuYX8DIgdB6ED890MhJiVK+y7iw0b+z1HQ7OVnYfLVRSUh+FoNNQ/6CL7usV3oVC4DGm12qOf/P0Zft99TkvWMhQaOG3YaQT5emnkjaURjmTDtKVuFSNo2jSC5szG/OqrRC5ehC4wsM9z7D5STqPFppzbHoajZqgPgY9aXp8DYYB21cMUim4oe2Mt9d9+y/A7/4rvsDinzFlcW0xORY53+ysKdoCl3qUhs90Ru2IFVrOZ8o39210Yck3odYKZqVFOlkwxEBxNyvtX+89CiPXAZ5pIpFB0Q2NePqV//zshZ59N2MUXO23erMIsAO/2V+RlgNDZK826maDp0wmaNcu+u1i0CF1AQO8XtcOQY2ZyUjihAV6aGDlI6W89gNHASGcKolD0hN38dAciIIDh997j1FpBBqOBuKA40iPSnTany8nfau9dEegZXY9jVizHWmqi4u23+3RdVUMzPxRUME/5KzwOR5sfVQshqlpfwH+xd8xTKFxC2ev/pP677xh+5534xjnH/ARgsVnILspmXsI87y1W11gDxp0uLfHRG8EzZxI0cybml1/B1tDg8HXb88qwSZir/BUeh6PRUKFSyrB2rzEnm6YUCq1ozM2l9OmnCfnZOYRd9Aunzr3HtIfqpmrvNkEdyQKbRfOS5H0lZsUKLKWlVLz9jsPXGHJN+PvomDYyUkPJFP3B0Z3FJS15Fq2fI4QQv9JOLIXCjrRYKPzLHegCA4m/x7nmJ7BHQemEjtnxXlyQID8D9P4w0rO+Q/CsmQRNn475lVewNTY6dE1WjpkZKVEE+Hpp7/NBjKM+i3uklJWtH6SUFcA92oikUJzA/I9/0PDDDwy7+y58NOhZYjAaOCXmFML9w3sf7KnkZcCImeDb9zBVrYlZuQLL8eNUvPNur2NLqxs5UFLNHJW17ZE4qiy6GudQJJVC0V8ac3IwPfMsoeeeS9iFFzp9/vKGcvaY9ni3CarWDCU/ekTIbFcEzZpF4GmnYX75ZWxNTT2Ozc6zl/hQLVQ9E0eVxU4hxP8KIUYJIdKEEE8Bu7QUTDG0aTM/hYQ4PfqplW1F25BI5id4cVe8w60lPjxTWQghiF25AktJCRXv9ry7yMoxERrgwymJXrzLG8Q4qixuBJqAjcDbQD2wQiuhFIqy11+n4ccfGX73XfhEa2OWyDRmEu4fzoRo7duPakZeBviFQsI0d0vSLUGzZxM4bRrml3reXRhyTcxOi0av89KotEGOo9FQtVLK1a39rqWUd0gptenQrhjyNB8/TunzLxByzjmEXXCBJmtIKckqzGJu/Fz0Oi92puZnQMo80HuuVVgIQcyK5ViKi6l8770uxxwrq+NYWT3zlL/CY3E0GmqLECKi3edIIcQn2omlGMqU/v1pZHMzw1bdrtkaB8sPYqo3ebe/ouIYlOV5rAmqPcFz5xI4ZQqm/3upy91FWwtV5a/wWBw1Q8W0REABIKUsB5yXGaVQtFC/Zy+V//43UUuvwG+kdkUC2rriJXhxV7zWkuQe6txuj313sQJLURGV7/270/msXDOxof6Mjgtxg3QKR3BUWdiEEG1/uUKIFLqoQqtQDAQpJSWPPoI+MpKYP/5R07UMhQbGRo4lNsj54bguIz8DgmIgdry7JXGI4PnzCDh1MqaX/g/ZbnchpSQr18zcUdHem0U/BHBUWfwVyBRCrBVCrAUygL9oJ5ZiKFL9yafU79xF7E03oQ8N1Wyd2uZavj3+rXeboKS0O7dTF4CuvyXeXIsQgtgVK7AUFlHxn/+0HT9YUoOpplHVg/JwHHVwbwamAwewR0Tdij0iSqFwCrbGRo4/8QT+Y8cS8dvfaLrW9qLtWGwWl5Ukb6htxtJsde6kpoNQU+wVJqj2BJ9+OgGTJ2P+v5eQzfZuzSf8Fcq57ck46uBehr2Pxa0tr7XAvdqJpRhqlP3zDZqNRob9ZTVCr210kqHQQKBPIFPjpmq6DkB9TRPr7t3G+vu3U5Jf5byJ8zLsP73Aud0e++5iOc1GI5Xvvw/YW6iOjAoiKdJLG08NERzdv94EzACOSCnPAqYCpZpJpRhSWEpLMb/4IiHnnEPwbG3rG0kpyTRmMit+Fr567fslZL2XS2OtBZvFxr+e2MXOjw9jsznB3ZefAREjISp14HO5mOAFCwiYNAnTCy/S3NDIN3llqiueF+CosmiQUjYACCH8pZQ/AWO1E0sxlDj+9NPYmpsZ9ufbNF/raPVRjDVGl5igCg9V8FNWEVPOHcmiu2Yyalos33yQx/tPfUt1meNluzths8Lhr71uV9FKa95Fs9HI/jfeprrRovptewGOKouCljyL/wBbhBDvA4XaiaUYKjTs20flv94j6ve/xy8lRfP1WkNmtXZuWy02MtYfIDQqgOm/SME/yJefXzORc64aT+nRajY+uJ1DO0v6N3nR99BQ6bXKAiDkzDMJmDgRyxuvobdZmauS8TweRx3cl0gpK6SU9wJ3Aa8CqkS5YkBIKSl55FH0ERHE3KBtqGwrWYVZJIclMyJ0hKbrfP/5McoKa1mwaAy+fnYfjBCCcbPjuezOGUQMC+LTV/by+T/30dRg6dvk+a3+Cs9pdtRXWncXgaZillTtIzrE390iKXqhzzF3UsoMKeUHUsqeS0gqFL1QvWULdTt2EHvTn9CHhWm+XqO1kR3FOzRPxKsy1bPjw3zSpsSSMrmzeSU8NohLbpvG9AtTOLCtmI0P7eib8zsvw55bETrMiVK7Hp/5C8iNSGThj58gLX1UmAqX4x0B2opBh62pieOPP4H/6NFE/Pa3Lllzd8lu6i31zE/UrsqslJKvNx4EnWD+paO7HafX65i1MI1f3TINm9XGe0/sYucmB5zflkY4us3rQma7YvfRCt4cey4h5mIqP/zQ3eIoekFTZSGEOF8IcUAIkSOEWN3F+aeEEN+1vA4KISpajk8RQmQLIfYKIX4QQlympZwK11P+xhs0FxQQt3oVwsc1RfCyCrPw1fkyfdh0zdbI/97E4R/NzLwoldCogF7HJ4yOYNGdM0mbFss37zvg/C7YAZZ6r/ZXtJKVa2JHwiR8x47D/MKLanfh4WimLIQQemANcAEwAVgshOhQC1pKebOUcoqUcgrwLNBakrIOWCqlnAicD/y9fSFDhXdjMZkwvfAiIWedRcg812VRZxozmTZsGkG+2sTzNzVY+HrjQaITQ5h8dpLD13Xl/M7ZdbzrwXkZIHSQ7MU1rVow5Jg5dUQEcSuX03TkCFUffeRukRQ9oOXOYiaQI6XMa/FvbAB+2cP4xcB6ACnlQSnloZb3hcBxwIuL+CjaU/r0M9gaG4m7/c8uW7O4tpicihxNGx3t+OgwNeWNnLFkLHp93/60TnZ+f/LyHj5/Y39n53d+BiRMhUDvfnaqamjmh4IK5qXHEHrOOfiPHYvphReRVidnuiuchpbKIhE41u5zQcuxTgghkoFU4Isuzs0E/IBcDWRUuJiG/fupePddoi6/HP9U1yWUZRdmA9qFzJoKavj+82NMmJ9A/Kj+d3rr4PzOLuro/G6sBuOuQWGC+iavDJuEuaNiEDodMcuX03T4MFUff+xu0RTdoKWy6Kp8ZHfeu0XAu1LKDo8VQoh47KVF/iCltHVaQIjrhBA7hRA7S0tVQrmn0xYqGx5OzPIbXLp2pjGTuKA40iPSnT63tEky1v2Ef5APcy4ZNeD5unV+52eBzTIonNuGHBP+PjqmjrTvkELP/Rn+o0djev4FtbvwULRUFgVA+2D2JLpP5FtEiwmqFSFEGPARcKeUcltXF0kpX2rt3hcbq6xUnk7N559Tt307MX+6EX246/osW2wWsouymZcwT5MS2PuziijOq2Leb9IJCHZeCZFOzu83a6iWCTBiltPWcBfZuWZmpEQR4NuSg6LTEbNiBU35+VRt2uxm6RRdoaWy2AGMFkKkCiH8sCuED04eJIQYC0QC2e2O+QH/Bt6QUr6joYwKF2FraqLk8SfwSx9F5KWXunTtPaY9VDdVa2KCqq9uIuu9HBJGRzB29nCnz9/m/L5yPKVlQWw0PUnOD9VOX8eVlFY3cqCkulOV2dCfn2vfXbygdheeiGbKQkppAVYCnwD7gbellHuFEPcLIRa2G7oY2CClbG+iuhRYAFzVLrR2ilayKrSnfO2bNB89yrDVf3FZqGwrhkIDOqFjdrzzixRm/SuH5kYrZywZq1njHiEE4yb7clnU/xARYene+e0lZOXaS5Kf3L/C7ru4gabcXKo2q92Fp6HpX62U8mPg45OO3X3S53u7uO5N4E0tZVO4DovZjOmFFwg54wxC5ru+4ZDBaOCUmFMI93eu6ct4sJyfthUz7fxkouKDnTp3J/K3Eu5TzCVXDWfH99Hs2nyEokMVnHv1RIalap/97kyycsyEBvgwKbHzv0foeefhlz4K0wsvEHbBBQgvaew0FFD/EgrNKX3mWWwNDcStut3la1c0VLDHtMfpJiirxUbGugOERgcw/cIUp87dJfkZ4B+GfsQ0Zv9yFJfcMhVrXzK/PQhDronZadHodZ13YkKnI+aGG2jKyaX6k0/cIJ2iO5SyUGhKw4EDVLzzDpFLFuOfluby9bOLspFIp5ck/+6zo5QX13UoFKgp+VsheR7o7caAhNGRfcv89hCOldVRUF7PvB6qzIadfz5+aWmYnn8eaesUBKlwE0pZKDRDSknJo4+iDw0ldvlyt8iQacwk3D+cidETnTZnlamenR8dJm1qLCmnuKAPQ8UxKMvrFDLbwfndW+a3h9DaQnVeeve/N6HXE7N8OY2Hcqj+dIurRFP0glIWCs2o+fJL6rK3EXPjjegjXJ9xLKUkqzCLufFz0euc8/QvpWTrhoMIneD0HgoFOpUeSpILIRg3J55L/zqD8LgeMr89BEOumbhQf9LjQnocF3aB2l14GkpZKDRBNjVR8thj+I0aReRlrg2VbeVg+UFM9SbmJjqvjlLed6Uc2WNm5sWphET2XijQOYtmQHAsxE3odkhEXBC//vM0TrsgmZ+yi3i7r2XPXYCUkuxcE3NHRfcaOSb0emJu+CONBw9S/dlnLpJQ0RNKWSg0oeytdTQfOcqw1asQvtr3uu6Ktq54TvJXNDVYyHz7ENFJIUw+y/FCgQNCSvvOInUB9HKD1et1J5zfFs9zfh8oqcZU0+RwC9WwCy/ELyUF0xq1u/AElLJQOB1LWRmm558neMHphJx+utvkyCrMYmzkWGKDnJPdv/3DfGoqGjlzyVh0fSwU2G9KD0BNSZ/qQSWMjuSyO2eSNtWznN+GHDNAp2S87mjbXRw4QPXnn2spmsIBlLJQOJ3SZ5/FVlfHsFWr3CZDbXMtu4/vdpoJylRQzQ9fFDBxfgLD01xXqoT8rfaffawHFRDsy8+XeZbzOzvXRHJ0EEmRjpeID/vFL/BNHmmvGSU9Y4c0VFHKQuFUGg4epGLj20QuXoz/qIEX1esv24u2Y7FZnFKSXNokX711gIBgH2b/ysXfKT8DIkZCZEqfL/Uk57fFauObvDKHTVCtCB8fYv54A43791PzRaei1AoXopSFwmlIKTn+6KPoQkOJWeGeUNlWDIUGAn0CmRo3dcBz7TMUUpLv/EKBvWKzwuGvB1ySvEvn92HXOr9/MFZS3WhhnoMmqPaEX3wRviNHUrpmjdpduBGlLBROo+arr6jNyiZ2xQp8IiPdJoeUkkxjJrPiZ+GrH9jNva6qiex/55I4JoIxs5xfKLBHir6DhkpIO3PAU3Vyfj++i12bXef8zmrJr5iT1ndlYd9d/JHGffup+fIrJ0umcBSlLBROQTY1cfyxx/FLTSVy8SK3ynK0+ijGGqNToqCy3tO+UGC35HWfX9Ff2ju/t/3Hdc5vQ46ZccNDiQ7x79f14QsvxnfECExqd+E2lLJQOIXy9etpOnzYraGyrRiMBmDgXfEKDpRzYFsxU38+ksjhGhcK7Ir8rfbcipA4p07raud3Q7OVXUfLe8za7g377uJ6Gvbupearr5wnnMJhlLJQDBhLeTmla54neP58ghc47ym4vxgKDSSHJTMidETvg7vBarGxdf0BwmICmH5BivOEcxRLIxzdplkLVVc6v3cdKafJYuuXv6I94QsX4puUZM+7ULsLl6OUhWLAmJ59DlttLcNW3e56U81JNFmb2FG8g7kJAwuZ/XZLa6HAsfi4olDgyRzbDpZ6p5qgusIVzm9Djgm9TjAzdWDKQvj6En39dTTs2UPt1q1Okk7hKEpZKAZE46FDlG/cSORll+E/2kW1knpg9/Hd1FvqmZ/Y/5DZytJ6dn58mFHTYkmeNLAbXL/JzwChgxTt+39o7fw25Jo5NSmcEP+Bt8+J+OUv8U1IoFTtLlyOUhaKfmOvKvsYuuBgYm5c6W5xALu/wlfny/Rh0/t1fWuhQJ1OMP93Y5wsXR/Iy4CEaRDgugRALZzflfXN/FhQMSB/RXuEnx/Rf7yehh9+oDYz0ylzKhxDKQtFv6ndupVag4HYFcvdGirbnkxjJtOGTSPI1/Es4fbkfVvK0b1mZi1MIySyf5E7A6axGoy7+py17Qxand9nLx3PcSc4v7fnl2GT9DkZrycifvUrfBLiMT2nIqNciVIWin4hm5spefQx/FJSiFy82N3iAFBSW0JORU6/s7abGix8vfEgMSNCOOXMRCdL1weOZIG0aubc7g0hBOPnxnOZE5zfhhwTAb46piU7r0S98PMj5rrrqf/+e2ozDU6bV9EzSlko+kX5+g005ecTt+p2hJ+fu8UB7IUDof8hs9s/yKe2qokzXFkosCvyMkDvDyNmuk8GnOP8zso1MSMlCn8f5wYJRPz6Enzi41XehQsZuMdJMeSwh8quIXjuXELOPNPd4rSRacwkLiiO9Ij0Pl9berSaH748xsTTExme6sJCgV2Rn8UmDzEAABqlSURBVAEjZ4FvoHvl4ITze+SEKLa8to9/PbaTwFA//IN8Wl6+9p+BPvgH29/7BfoQEORLHTYqjLX8asxwGust+PnrEV303e4P9t3FtRTfdz+1WVmEzNM+EGCoo5SFos+Y1jyPrbqauNWr3B4q24rFZmFb0TbOGXlOn2Wy2SRfrTtAQIgvs3/p+j7hHagphZI9cPZd7pXjJFqd3z98WUBteQONdRYa6y3UVTVRXlzb9pmTHvKvJAD5XyOv/NeIEOAXeJKSaX0f6IN/cIvSaTnnF2RXOn4t5/Qn7fbCf/MbTP/3EqY1zxM8d67H/F8crChloegTjbm5lK9fT8RllxIwxo3RQiexx7SHqqaqfpmg9mUWcvxwFT/7wwTXFgrsisNf23+mnelOKbokINiXmReldnte2iRNjVYaa5tprLfw7KYDfJ9XxsMXTaKp3kJjXTNNdRYa6ixtn2srGu2Kps6C1dJzgyMff32LMjmhZFhwG5bd2yh48WtCx6R23vG07HR8/fVKmQwQpSwUfaLkscfQBQURe+ON7halA4ZCAzqhY3b87D5dV1fVxLb/5JI4NpIxM4dpJF0fyM8A/zCIn+JuSfqM0An7zTzQflv5oryKCROimDg/waHrLc1Wu+Kote9SGuua2xRJY11zyzELjbXNNNVbqC5roMkSQX38HAq+t8D3h7qdW6cT+Af7tOxsWnYuAXpgcCiQiLhAzcvnK2WhcJiarVup3fo1catW4RMV5W5xOpBlzOKUmFMI9++bv8Hwr0M0N1k5Y/EYz3jyzMuA5Hmg9+4/zaPmOgrK67n2dMfNej6+enzC9QSH9y1kuezNtyh66GHiX3wV/YTJ7RRMO4VTf+Jz6+6mxgO6BzoL4YJ4DO/+H6lwGbK5mZLHHsc3eSRRly9xtzgdqGio4EfTj9ww5YY+XVfwUxkHvylh+oUp7ikUeDIVR6E8H2Zd725JBowh116SfKD1oBwh4ne/xfzSS1S/vIbkN9dqvt5QRYXOKhyifOPbNOXmMmzVKo8JlW0luygbiexTSXJrs42M9QcJiwngtPOTNZSuD7SVJHdPfoUzMeSYiAv1Z1RsiOZr6fz9iV62jLqdO6n9Zrvm6w1VlLJQ9Iq1ogLTs88SNGc2IWed5W5xOmEwGgj3D2di9ESHr/l2yxEqSupYsNhNhQK7Ij8DguMgbry7JRkQNpskO9fM3FHRLjPtRVz6O/SxMZjWrHHJekMRTZWFEOJ8IcQBIUSOEGJ1F+efEkJ81/I6KISoaHfuSiHEoZbXlVrKqeiZ0uefx1pdzbDVqz3Drt8OKSWGQgNz4+ei1zl2068srWPnx0dIPy2O5IluKhR4MlLa+1ekLgAP+x33lYPHqzHXNjHXSfWgHEEXEEDMsmXUbd9O3Y4dLlt3KKGZshBC6IE1wAXABGCxEGJC+zFSypullFOklFOAZ4H3Wq6NAu4BZgEzgXuEEJ5RfGiI0ZiXT/m69UT87ncEjB3rbnE6cbD8IKZ6E3MTHStJ3lYo0Ecw/3fur5LbRukBqClxSz0oZ2PIMQM4rXigo0Rcdhn62BhK1zzv0nWHClruLGYCOVLKPCllE7AB+GUP4xcD61venwdskVKWSSnLgS3A+RrKquiG448/ji4ggNg/eVaobCuGwpaueA76K3J3l3J0bxmzFqYRHOGmQoFdke/8FqruIivHREp0EIkRrs1A1wUEEH3NNdRt20bdrl0uXXsooKWySASOtftc0HKsE0KIZCAV+KKv1yq0oybTQM1XXxFzww34RHuIueYkDEYDYyPH/n97dx5dVX0tcPy7b+Z5gCQyg4LIVKZgguBQ26KtLdpqq1bFoYN2vVZ9XbWtr2+1fXZ1aau2Wq2t2qqtrfqK1rH24UgwCQlDFEEQSUBCgpCBJGQebvb74x4wQiAhyc2552Z/1sri3uO5N/uYcDe/4exNRnxGn+d2tHbx1j8+IGNiEnPODrFfp515kDoJ0ia7HcmgdPm7Kd51gMVDWGX2RKRdeikRo23tIhiCmSx6m3g9VsWvy4CnVdV/Iq8VkW+LyAYR2VBdXT3AME1vtKuLql/dQdTEiaRddaXb4fSqpbOFkqqSfk9BFb+4k5ZQKBR4JH8XfJgfFlNQmyoaaGrvGpYts73xxcUx6rrraC5cS0tJiSsxhKtg/o2pAHo2QR4P7D3GuZfx8RRUv1+rqg+paraqZmdk9P0vS9N/9StX0r6jlKwf3oIvxLbKHrJu3zq6urv6VZK8uryRzW9WMPuscWRNTh6G6E7Avk3Q3hAWW2bXOvdXLD7ZvZFo2mWXEpGeTs39NroYSsFMFuuBaSIyRUSiCSSEF448SUSmA2nA2h6HVwHLRCTNWdhe5hwzw8B/8CDV9/6O+JwcEj/zGbfDOab8ynziIuOYnzn/uOd1dyur//4+sUnR7hcK7M3O8FmvKCitZcaYZEYlurce5IuPZ9Q3rqO5sJCWt992LY5wE7RkoapdwHcJfMhvA/6hqu+JyG0isrzHqZcDT2mPovSqegD4BYGEsx64zTlmhkHNA3/A39BA1q2ht1W2p4LKAnJOyiEq4vjF/7a+VUnV7kaWXjI1UHwu1OzKg8yZkJjpdiSD0tbpZ2N5HUtOcX99K+3yy4lIS6PGdkYNmaCW+1DVl4GXjzj20yOe//wYr30EeCRowZlete/axYG//Y3USy4h9rTT3A7nmMoPllPRVMHVs45/C05zQztrn9vJ+NPSmLYoBAoFHqmzDcqLYOG1bkcyaBs+rKOjq5szXFqv6OnQ6KLqrrtp3bSJuLlz3Q7J80Jolc+Egqo778IXE0PGTTe6Hcpx5VfmA313xSt4upSuTj9nXz49NEdJFeugqy0sFrcLymqI9AmnT3E/WcDHo4tq2xk1JCxZmMOaCwtpeuMNRn/nBiJHu7P1sb8K9hYwMWkiE5ImHPOcPe8fYMf6/Sw4bxKpWfHDGN0J2LUGJCJQadbjCstqmTshlcSY0KhP6ktIIP3aa2le8xat777rdjieZ8nCAIGtsvtvv4OoCRNIW7HC7XCOq8Pfwfp96487qujq9JP3xHaSM+JCp1Bgb3bmwbgFEBtiO7ROUENrJ5sr6kNivaKntK9/nYiUFBtdDAFLFgaA+qefoX3HDjJv+UHIbpU9pKSqhNauVpaOO/aW2bdfKaehqpWzLz+VyKgQKRR4pLaDULkxLHZBFe+spVsZ1npQ/RGR6Iwu8tbQunmz2+F4miULg7+xkep77yV+0SKSPvc5t8PpU0FlAVG+KLKzsnv97/VVLWz8926mZmcycWZo/Uv3E3YXgvrD4v6KwrJaYqN8zJ+Y6nYoR0m78gp8KSm2M2qQLFkYav7wR/z19SG/VfaQgr0FLMhaQHzU0esQhwoFRkQKSy8JoUKBvdmVB5GxMCHH7UgGraC0hkWT04mJDL1RXERiIqOuvYam1atp3fKe2+F4liWLEa5j924OPP44KV/5MrEzZ/b9Apftb97Pjrodx7xru3RjFXu2HiDnwlNCq1Bgb3atCSSKqFi3IxmUqsY2dlQ1cYZL9aD6I+3KKwOjiwdsdDFQlixGuP133okvKoqMm25yO5R+KdxbCNBrPaj21i7yV+4gY2ISs0OtUOCRmqph/5aw2DK7tuxQSfLQnfKLSEwk/eoVNL3xBm1bt7odjidZshjBmouKaHrtdUZdfz1Rmd64e7hgbwGZcZlMSz16iqn4hUChwHOumI7PF+LTaR+uCfw55RxXwxgKBaU1JMdGMmtsituhHFf6VVfhS062fhcDZMlihFK/P7BVduxY0q/xRiPCru4u1u5dy5JxS45aW6nafZAtqyuYc/Z4Mid5YBvqzjyISYYx3r6zWFUpKK0l9+RRRIR4go5ISiJ9xQqaXn+dtm3b3A7HcyxZjFD1zzxD+/btZP7wFnwxIT6379hSs4WDHQePmoLq7lbynthOXFI0OaFYKLA3u/Jg8lKICI0b2Aaq/EALlfWtw94Vb6DSV1yFLynJ1i4GwJLFCORvbKT6nnuJW7iQpPPOczucfivcW4hPfCwes/gTx99b4xQK/Oo0YuI88OFbtxvqPgyLLbMft1AN3fWKniKSk0lfsYLGV1+jbft2t8PxFEsWI1Dtgw/ir6sj69ZbPbFV9pCCygLmjJ5DSszHc+PNDe0UPVfGhBlpTM32xroLu5z1ijBY3C4sqyEzKYZTMhLdDqXf0ldchS8xkep77qW7udntcDzDksUI01FezoG//JWUiy4ibvYst8Ppt/q2ejbXbD6q13bB06X4u5SzLgvRQoG92ZUHCZmQEbpVffuju1tZW1bLkqmjvfP/HohISSH9umtpevNNtufk8uEVV1L9u/toWb+e7o4Ot8MLWR4Ys5uhVHXnXRAVRcbNN7sdygkp+qgIRT9RD2rP1kChwEVfnBK6hQKPpBoYWUw5Czz0Adub7fsbqW3u4IwQqwfVH6O/8x3i582jeW0RzcXF1Pzxj9Q88AASG0v8woXE5+aQkLuY2JkzkIjQu9HQDZYsRpDm4nU0vvoqGTffRFSWR6ZsHPmV+aTEpDBrVGA01NXpJ+/J7aRkxrHgvIkuR3cCqt+Hpv1hMQVVUBpooRpq9aD6Q0RIOOMMEs4IbJbwHzxIy4YNNK8toqWoiOq7f0M14EtOJv70RSTk5JKwOJfoU07x1ChqKFmyGCHU72f/HXcQOXYM6ddc43Y4J0RVKdxbyOIxi4nwBf6VV7KqnIbqVpbfNC90CwX25nALVe8ni8KyWiaPimdcapzboQxaRHIySeeeS9K55wLQVVNDc3ExLUVFNBcV0/Ta64HzMkYfThzxOblEjw/xmz+HkCWLEaLh2Wdp37aNcb+5G1+st8pLfFD3AdWt1YenoOr3t7Dx/z5k2qIsJsxIdzm6E7RrDaRNhrQQLpveD53+bop31nLh/PD8sIwcPZqUCy4g5YILAOioqKSluMiZtiri4EsvARA1YQIJuTnE5+aSkJMT8n1gBsOSxQjgb2qi6p57iZs/n6TPf97tcE5Ywd4CAJaMXYKqkvfkdiKjIlhyyVSXIztB/i74MB9mXeR2JIP2bkUDzR1+loRwPaihFD1+HNHjLyb14otRVTrKymguKqa5aC0HV71C/cqnAYiZNpX43MWBkUd2NhHJHrhBtJ8sWYwAtQ8+hL+mhqw/PODJ+daCygJOTTuVjPgMdqzfT8X7dZx12akkpHjjZsLDPtoE7Q1hsV5R6KxXLPbg4vZgiQgxU6cSM3Uq6Vdegfr9tG3dRnPRWlqKiqlfuZK6xx8Hn4/Y2bNJyMkhYXEucfPn44vz7pSdJYsw11FRwYHHHiPlwguJmzPH7XBOWEtnCyVVJVw18yraWzrJX7mDzElJzDrLg9Mfu1YH/pzs/WZHBWU1zBiTTHpCaDfKGg4SEUHcnNnEzZkN3/oW3R0dtG3adHinVe2jj1L78MNIVBRx8+YRvziXhNzFxM2ZjURFuR1+v1myCHNVd94FkZFkfP8/3Q5lQNbtW0dXdxdLxy6l+PmdtDZ2cMF/fCr0CwX2ZmceZM6CxAy3IxmU1g4/JbvrWbHY2+suweKLjiZ+0SLiFy0ig+/R3dxMS0nJ4Z1WNffdT83v7sMXH0/couzDC+Yx06cjvtC99c2SRRhrWb+exlWrGH3j94jKynI7nAHJr8wnLjKO8a1TeXbNO8w5xyOFAo/U2QZ7iiH7OrcjGbSNu+vo8Hd7ph6U23wJCSSeeSaJZ54JQFddHS3r1x/eaVWV92sAIlJTic/JObxgHj15ckhNG1uyCFPq97Pv9tuJHDOGUdde63Y4A1a4t5CcrBzynyojPjmanOUeKRR4pIp10NUWFltmC8pqiPQJp0/x2E60EBGZlkbysmUkL1sGQOf+qo93WhUV0bhqVeC8rCwScnMDO60W5xJ10kluhm3JIlw1PPc87Vu3Mfauuzy7qFZ+sJw9jXv4asf1VJc3suybs7xRKLA3O/NAImDS0U2bvKawtIZ5E1JJiPHozyLERGVlkrJ8OSnLl6OqdJaXH95p1bRmDQ3PPw9A9KRJznpHLvE5OUSmpQ1rnPbTDkP+pmaq7vktcXPnknzBF9wOZ8DyK/OJ70imoySFCTNTmbrQW3edf8KuPBi3AGI9OIXWQ0NrJ5srG/jupz22bdkjRIToSZOInjSJtEu/hnZ3075jR2DKam0RB198ifqn/heAmNNOc0YeOcRnLyIiMSGosVmyCEO1Dz+Mv7qGrPvvD6k5zxNVuLeQz1ZegfrhrMtO9e61tB2EyhJY6s1NBj0V7aylW71Z4sOLxOcjdvp0YqdPJ/3qq9GuLtq2bHFGHkXUPfEEBx57jJhp0zj5xReCGoslizDTUVHJgUcfJXn5l4ib690ubB3+Diq21TFj32ks/NIkUjM9UiiwN7sLQf1hcX/F2rJaYqN8zJ+Y6nYoI5JERhI3bx5x8+Yx+obr6W5vp/Xtt+luaQ3697ZkEWaq7r4LfD4yv/99t0MZlA2VGzl9x3Ki02HBMo9v0dyVB5GxMP50tyMZtILSGhZNTicm0kP1uMKYLyaGhNzcYfleQU0WInI+cC8QAfxJVe/o5ZyvAT8HFNikql93jv8auIBAz41XgZtUVYc6xtJNG1j92/DpmBXpz6bp9Gyeu+N1t0MZFF93NCkdGcxMvJ89t9/odjiDktVdzdaI6fz4vmK3QxkUBUqrmrh44Xi3QzEuCFqyEJEI4PfA54AKYL2IvKCqW3ucMw24FViiqnUikukcPwNYAnzKOTUfOBtYPdRxRsbEIrpvqN/WNe3RQkOqD/XiTWtH8I0qJi6hkwNMcTuUQTnAFN5KWc60eO90kzuW2WOTuWieB++eN4MWzJHF6UCpqu4EEJGngAuBrT3O+Rbwe1WtA1DVKue4ArFANCBAFLA/GEFOPm023/jL7GC8tTGHLXA7AGMGKZj3lo8D9vR4XuEc6+lU4FQRKRCRImfaClVdC7wJfOR8rVLVbUd+AxH5tohsEJEN1dXVQbkIY4wxwU0Wvc2DHLnmEAlMA84BLgf+JCKpIjIVmAGMJ5BgzhWRo6qvqepDqpqtqtkZGd6ut2OMMaEsmMmiApjQ4/l4YG8v5zyvqp2qugvYTiB5fBkoUtUmVW0C/g0Mz5K/McaYowQzWawHponIFBGJBi4Djrxr5Dng0wAiMprAtNROoBw4W0QiRSSKwOL2UdNQxhhjhkfQkoWqdgHfBVYR+KD/h6q+JyK3ichy57RVQK2IbCWwRnGLqtYCTwNlwGZgE4EttS8GK1ZjjDHHJ0G4dcEV2dnZumHDBrfDMMYYTxGRjaqa3dd5odtpwxhjTMiwZGGMMaZPYTMNJSLVwO5BvMVooGaIwnFTuFwH2LWEqnC5lnC5DhjctUxS1T7vPQibZDFYIrKhP/N2oS5crgPsWkJVuFxLuFwHDM+12DSUMcaYPlmyMMYY0ydLFh97yO0Ahki4XAfYtYSqcLmWcLkOGIZrsTULY4wxfbKRhTHGmD5ZsnCIyC9E5F0ReUdEXhGRsW7HNFAicqeIvO9cz7Mi4tmGySLyVRF5T0S6RcRzO1dE5HwR2S4ipSLyY7fjGQwReUREqkRki9uxDIaITBCRN0Vkm/O7dZPbMQ2UiMSKyDoR2eRcy/8E7XvZNFSAiCSr6kHn8Y3ATFW9weWwBkRElgFvqGqXiPwKQFV/5HJYAyIiM4Bu4EHgB6rqmZouTrfID+jRLRK4vGe3SC9x2gQ0AX9VVc92DBORMcAYVS0RkSRgI3CRF38uIiJAgqo2OUVX8wm0oC4a6u9lIwvHoUThSODo3hueoaqvOIUcAYoIlIf3JFXdpqpebZJ+uFukqnYAh7pFepKqrgEOuB3HYKnqR6pa4jxuJFDo1JO9YjWgyXka5XwF5bPLkkUPIvJLEdkDXAH81O14hsh1BPqBmOHXn26RxkUiMhmYDxS7G8nAiUiEiLwDVAGvqmpQrmVEJQsReU1EtvTydSGAqv5EVScAfydQXj1k9XUtzjk/AboIXE/I6s+1eFR/ukUal4hIIvAMcPMRMwueoqp+VZ1HYAbhdBEJyhRhZDDeNFSp6mf7eeoTwL+AnwUxnEHp61pE5Grgi8BnNMQXpk7g5+I1/ekWaVzgzO8/A/xdVf/pdjxDQVXrRWQ1cD4w5JsQRtTI4nhEZFqPp8uB992KZbBE5HzgR8ByVW1xO54RrD/dIs0wcxaF/wxsU9XfuB3PYIhIxqHdjiISB3yWIH122W4oh4g8A0wnsPNmN3CDqla6G9XAiEgpEAPUOoeKPLyz68vAfUAGUA+8o6rnuRtV/4nIF4B7gAjgEVX9pcshDZiIPAmcQ6DC6X7gZ6r6Z1eDGgARWQq8RaATZ7dz+L9U9WX3ohoYEfkU8BcCv18+Ah1JbwvK97JkYYwxpi82DWWMMaZPliyMMcb0yZKFMcaYPlmyMMYY0ydLFsYYY/pkycKYEyAiTX2fddzXPy0iJzuPE0XkQREpcyqGrhGRHBGJdh6PqJtmTWizZGHMMBGRWUCEqu50Dv2JQGG+aao6C7gGGO0UHXwduNSVQI3phSULYwZAAu50alhtFpFLneM+EXnAGSm8JCIvi8glzsuuAJ53zjsFyAH+W1W7AZzqtP9yzn3OOd+YkGDDXGMG5ivAPGAugTua14vIGmAJMBmYA2QSKH/9iPOaJcCTzuNZBO5G9x/j/bcAi4ISuTEDYCMLYwZmKfCkU/FzP5BH4MN9KbBSVbtVdR/wZo/XjAGq+/PmThLpcJrzGOM6SxbGDExv5cePdxygFYh1Hr8HzBWR4/0djAHaBhCbMUPOkoUxA7MGuNRpPJMBnAWsI9DW8mJn7SKLQOG9Q7YBUwFUtQzYAPyPUwUVEZl2qIeHiIwCqlW1c7guyJjjsWRhzMA8C7wLbALeAH7oTDs9Q6CPxRYCfcOLgQbnNf/ik8njm8BJQKmIbAYe5uN+F58GPFcF1YQvqzprzBATkURVbXJGB+uAJaq6z+k38Kbz/FgL24fe45/ArR7uP27CjO2GMmboveQ0pIkGfuGMOFDVVhH5GYE+3OXHerHTKOk5SxQmlNjIwhhjTJ9szcIYY0yfLFkYY4zpkyULY4wxfbJkYYwxpk+WLIwxxvTJkoUxxpg+/T/THLH8cozi2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203ed16c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_gamms = len(gammas)\n",
    "\n",
    "test_scores =  np.array(test_means).reshape(n_Cs,number_gamms)\n",
    "#train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_gamms)\n",
    "#train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(gammas):\n",
    "    plt.plot(x_axis, test_scores[:,i], label= gammas[i])\n",
    "    #plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = gammas[i] )\n",
    "    #plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuary' )\n",
    "plt.savefig('SVMGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选出最佳的参数gmma"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
